Build a Large Language Model (from Scratch)
Paperback
Series: From Scratch
ISBN13: 9781633437166
Publisher: Manning Publications
Published: Oct 29 2024
Pages: 368
Weight: 1.35
Height: 0.90 Width: 7.40 Depth: 9.20
Language: English
- 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
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