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Generative AI with Python and PyTorch

You're reading from   Generative AI with Python and PyTorch Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications

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Product type Paperback
Published in Mar 2025
Publisher Packt
ISBN-13 9781835884447
Length 450 pages
Edition 2nd Edition
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Authors (2):
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Joseph Babcock Joseph Babcock
Author Profile Icon Joseph Babcock
Joseph Babcock
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
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Table of Contents (18) Chapters Close

Preface 1. Introduction to Generative AI: Drawing Data from Models 2. Building Blocks of Deep Neural Networks FREE CHAPTER 3. The Rise of Methods for Text Generation 4. NLP 2.0: Using Transformers to Generate Text 5. LLM Foundations 6. Open-Source LLMs 7. Prompt Engineering 8. LLM Toolbox 9. LLM Optimization Techniques 10. Emerging Applications in Generative AI 11. Neural Networks Using VAEs 12. Image Generation with GANs 13. Style Transfer with GANs 14. Deepfakes with GANs 15. Diffusion Models and AI Art 16. Other Books You May Enjoy
17. Index

LLM Foundations

It might feel like Large Language Models (LLMs) have dominated the AI landscape for a long time, but in reality, it’s only been a couple of years. The AI craze truly took off when OpenAI released ChatGPT in November 2022, reaching a million users within just a week1. This was a remarkable feat—especially considering the closest comparison is Instagram, which took eight weeks to hit a million downloads2. The previous most pivotal moment in AI came in 2012, when AlexNet won the ImageNet competition3, though that breakthrough mostly resonated within academic circles.

In this chapter, we will expand on our understanding of NLP concepts and explore what sets LLMs apart from the models we’ve discussed so far. Specifically, we will cover:

  • A brief recap of transformer architectures
  • The LLM training setup and the role of InstructGPT
  • Hands-on exercises to apply these learnings

All the code snippets presented in...

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