فهرست مطالب

• brief contents
• contents
• foreword
• preface
• acknowledgments
• about the book
• Who should read this book
• How this book is organized
• About the code
• liveBook Discussion Forum
• about the authors
• about the cover illustration
• Chapter 1: Words’ awakening: Why large language models have captured attention
• 1.1 Large language models accelerating communication
• 1.2 Navigating the build-and-buy decision with LLMs
• 1.3 Debunking myths
• Summary
• Chapter 2: Large language models: A deep dive into language modeling
• 2.1 Language modeling
• 2.2 Language modeling techniques
• 2.3 Attention is all you need
• 2.4 Really big transformers
• Summary
• Chapter 3: Large language model operations: Building a platform for LLMs
• 3.1 Introduction to large language model operations
• 3.2 Operations challenges with large language models
• 3.3 LLMOps essentials
• 3.4 LLM operations infrastructure
• Summary
• Chapter 4: Data engineering for large language models: Setting up for success
• 4.1 Models are the foundation
• 4.2 Evaluating LLMs
• 4.3 Data for LLMs
• 4.4 Text processors
• 4.5 Preparing a Slack dataset
• Summary
• Chapter 5: Training large language models: How to generate the generator
• 5.1 Multi-GPU environments
• 5.2 Basic training techniques
• 5.3 Advanced training techniques
• 5.4 Training tips and tricks
• Summary
• Chapter 6: Large language model services: A practical guide
• 6.1 Creating an LLM service
• 6.2 Setting up infrastructure
• 6.3 Production challenges
• 6.4 Deploying to the edge
• Summary
• Chapter 7: Prompt engineering: Becoming an LLM whisperer
• 7.1 Prompting your model
• 7.2 Prompt engineering basics
• 7.3 Prompt engineering tooling
• 7.4 Advanced prompt engineering techniques
• Summary
• Chapter 8: Large language model applications: Building an interactive experience
• 8.1 Building an application
• 8.2 Edge applications
• 8.3 LLM agents
• Summary
• Chapter 9: Creating an LLM project: Reimplementing Llama 3
• 9.1 Implementing Meta’s Llama
• 9.2 Simple Llama
• 9.3 Making it better
• 9.4 Deploy to a Hugging Face Hub Space
• Summary
• Chapter 10: Creating a coding copilot project: This would have helped you earlier
• 10.1 Our model
• 10.2 Data is king
• 10.3 Build the VS Code extension
• 10.4 Lessons learned and next steps
• Summary
• Chapter 11: Deploying an LLM on a Raspberry Pi: How low can you go?
• 11.1 Setting up your Raspberry Pi
• 11.2 Preparing the model
• 11.3 Serving the model
• 11.4 Improvements
• Summary
• Chapter 12: Production, an ever-changing landscape: Things are just getting started
• 12.1 A thousand-foot view
• 12.2 The future of LLMs
• 12.3 Final thoughts
• Summary
• Appendix A: History of linguistics
• A.1 Ancient linguistics
• A.2 Medieval linguistics
• A.3 Renaissance and early modern linguistics
• A.4 Early 20th-century linguistics
• A.5 Mid-20th century and modern linguistics
• Appendix B: Reinforcement learning with human feedback
• Appendix C: Multimodal latent spaces
• Index