Quick Start Guide to Large Language Models
portes grátis
Quick Start Guide to Large Language Models
Strategies and Best Practices for Using ChatGPT and Other LLMs
Ozdemir, Sinan
Pearson Education (US)
10/2023
288
Mole
Inglês
9780138199197
15 a 20 dias
Descrição não disponível.
Foreword xv
Preface xvii
Acknowledgments xxi
About the Author xxiii
Part I: Introduction to Large Language Models 1
Chapter 1: Overview of Large Language Models 3
What Are Large Language Models? 4
Popular Modern LLMs 20
Domain-Specific LLMs 22
Applications of LLMs 23
Summary 29
Chapter 2: Semantic Search with LLMs 31
Introduction 31
The Task 32
Solution Overview 34
The Components 35
Putting It All Together 51
The Cost of Closed-Source Components 54
Summary 55
Chapter 3: First Steps with Prompt Engineering 57
Introduction 57
Prompt Engineering 57
Working with Prompts Across Models 65
Building a Q/A Bot with ChatGPT 69
Summary 74
Part II: Getting the Most Out of LLMs 75
Chapter 4: Optimizing LLMs with Customized Fine-Tuning 77
Introduction 77
Transfer Learning and Fine-Tuning: A Primer 78
A Look at the OpenAI Fine-Tuning API 82
Preparing Custom Examples with the OpenAI CLI 84
Setting Up the OpenAI CLI 87
Our First Fine-Tuned LLM 88
Case Study: Amazon Review Category Classification 93
Summary 95
Chapter 5: Advanced Prompt Engineering 97
Introduction 97
Prompt Injection Attacks 97
Input/Output Validation 99
Batch Prompting 103
Prompt Chaining 104
Chain-of-Thought Prompting 111
Revisiting Few-Shot Learning 113
Testing and Iterative Prompt Development 123
Summary 124
Chapter 6: Customizing Embeddings and Model Architectures 125
Introduction 125
Case Study: Building a Recommendation System 126
Summary 144
Part III: Advanced LLM Usage 145
Chapter 7: Moving Beyond Foundation Models 147
Introduction 147
Case Study: Visual Q/A 147
Case Study: Reinforcement Learning from Feedback 163
Summary 173
Chapter 8: Advanced Open-Source LLM Fine-Tuning 175
Introduction 175
Example: Anime Genre Multilabel Classification with BERT 176
Example: LaTeX Generation with GPT2 189
Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 193
The Ever-Changing World of Fine-Tuning 206
Summary 207
Chapter 9: Moving LLMs into Production 209
Introduction 209
Deploying Closed-Source LLMs to Production 209
Deploying Open-Source LLMs to Production 210
Summary 225
Part IV: Appendices 227
Appendix A: LLM FAQs 229
Appendix B: LLM Glossary 233
Appendix C: LLM Application Archetypes 239
Index 243
Preface xvii
Acknowledgments xxi
About the Author xxiii
Part I: Introduction to Large Language Models 1
Chapter 1: Overview of Large Language Models 3
What Are Large Language Models? 4
Popular Modern LLMs 20
Domain-Specific LLMs 22
Applications of LLMs 23
Summary 29
Chapter 2: Semantic Search with LLMs 31
Introduction 31
The Task 32
Solution Overview 34
The Components 35
Putting It All Together 51
The Cost of Closed-Source Components 54
Summary 55
Chapter 3: First Steps with Prompt Engineering 57
Introduction 57
Prompt Engineering 57
Working with Prompts Across Models 65
Building a Q/A Bot with ChatGPT 69
Summary 74
Part II: Getting the Most Out of LLMs 75
Chapter 4: Optimizing LLMs with Customized Fine-Tuning 77
Introduction 77
Transfer Learning and Fine-Tuning: A Primer 78
A Look at the OpenAI Fine-Tuning API 82
Preparing Custom Examples with the OpenAI CLI 84
Setting Up the OpenAI CLI 87
Our First Fine-Tuned LLM 88
Case Study: Amazon Review Category Classification 93
Summary 95
Chapter 5: Advanced Prompt Engineering 97
Introduction 97
Prompt Injection Attacks 97
Input/Output Validation 99
Batch Prompting 103
Prompt Chaining 104
Chain-of-Thought Prompting 111
Revisiting Few-Shot Learning 113
Testing and Iterative Prompt Development 123
Summary 124
Chapter 6: Customizing Embeddings and Model Architectures 125
Introduction 125
Case Study: Building a Recommendation System 126
Summary 144
Part III: Advanced LLM Usage 145
Chapter 7: Moving Beyond Foundation Models 147
Introduction 147
Case Study: Visual Q/A 147
Case Study: Reinforcement Learning from Feedback 163
Summary 173
Chapter 8: Advanced Open-Source LLM Fine-Tuning 175
Introduction 175
Example: Anime Genre Multilabel Classification with BERT 176
Example: LaTeX Generation with GPT2 189
Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 193
The Ever-Changing World of Fine-Tuning 206
Summary 207
Chapter 9: Moving LLMs into Production 209
Introduction 209
Deploying Closed-Source LLMs to Production 209
Deploying Open-Source LLMs to Production 210
Summary 225
Part IV: Appendices 227
Appendix A: LLM FAQs 229
Appendix B: LLM Glossary 233
Appendix C: LLM Application Archetypes 239
Index 243
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Foreword xv
Preface xvii
Acknowledgments xxi
About the Author xxiii
Part I: Introduction to Large Language Models 1
Chapter 1: Overview of Large Language Models 3
What Are Large Language Models? 4
Popular Modern LLMs 20
Domain-Specific LLMs 22
Applications of LLMs 23
Summary 29
Chapter 2: Semantic Search with LLMs 31
Introduction 31
The Task 32
Solution Overview 34
The Components 35
Putting It All Together 51
The Cost of Closed-Source Components 54
Summary 55
Chapter 3: First Steps with Prompt Engineering 57
Introduction 57
Prompt Engineering 57
Working with Prompts Across Models 65
Building a Q/A Bot with ChatGPT 69
Summary 74
Part II: Getting the Most Out of LLMs 75
Chapter 4: Optimizing LLMs with Customized Fine-Tuning 77
Introduction 77
Transfer Learning and Fine-Tuning: A Primer 78
A Look at the OpenAI Fine-Tuning API 82
Preparing Custom Examples with the OpenAI CLI 84
Setting Up the OpenAI CLI 87
Our First Fine-Tuned LLM 88
Case Study: Amazon Review Category Classification 93
Summary 95
Chapter 5: Advanced Prompt Engineering 97
Introduction 97
Prompt Injection Attacks 97
Input/Output Validation 99
Batch Prompting 103
Prompt Chaining 104
Chain-of-Thought Prompting 111
Revisiting Few-Shot Learning 113
Testing and Iterative Prompt Development 123
Summary 124
Chapter 6: Customizing Embeddings and Model Architectures 125
Introduction 125
Case Study: Building a Recommendation System 126
Summary 144
Part III: Advanced LLM Usage 145
Chapter 7: Moving Beyond Foundation Models 147
Introduction 147
Case Study: Visual Q/A 147
Case Study: Reinforcement Learning from Feedback 163
Summary 173
Chapter 8: Advanced Open-Source LLM Fine-Tuning 175
Introduction 175
Example: Anime Genre Multilabel Classification with BERT 176
Example: LaTeX Generation with GPT2 189
Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 193
The Ever-Changing World of Fine-Tuning 206
Summary 207
Chapter 9: Moving LLMs into Production 209
Introduction 209
Deploying Closed-Source LLMs to Production 209
Deploying Open-Source LLMs to Production 210
Summary 225
Part IV: Appendices 227
Appendix A: LLM FAQs 229
Appendix B: LLM Glossary 233
Appendix C: LLM Application Archetypes 239
Index 243
Preface xvii
Acknowledgments xxi
About the Author xxiii
Part I: Introduction to Large Language Models 1
Chapter 1: Overview of Large Language Models 3
What Are Large Language Models? 4
Popular Modern LLMs 20
Domain-Specific LLMs 22
Applications of LLMs 23
Summary 29
Chapter 2: Semantic Search with LLMs 31
Introduction 31
The Task 32
Solution Overview 34
The Components 35
Putting It All Together 51
The Cost of Closed-Source Components 54
Summary 55
Chapter 3: First Steps with Prompt Engineering 57
Introduction 57
Prompt Engineering 57
Working with Prompts Across Models 65
Building a Q/A Bot with ChatGPT 69
Summary 74
Part II: Getting the Most Out of LLMs 75
Chapter 4: Optimizing LLMs with Customized Fine-Tuning 77
Introduction 77
Transfer Learning and Fine-Tuning: A Primer 78
A Look at the OpenAI Fine-Tuning API 82
Preparing Custom Examples with the OpenAI CLI 84
Setting Up the OpenAI CLI 87
Our First Fine-Tuned LLM 88
Case Study: Amazon Review Category Classification 93
Summary 95
Chapter 5: Advanced Prompt Engineering 97
Introduction 97
Prompt Injection Attacks 97
Input/Output Validation 99
Batch Prompting 103
Prompt Chaining 104
Chain-of-Thought Prompting 111
Revisiting Few-Shot Learning 113
Testing and Iterative Prompt Development 123
Summary 124
Chapter 6: Customizing Embeddings and Model Architectures 125
Introduction 125
Case Study: Building a Recommendation System 126
Summary 144
Part III: Advanced LLM Usage 145
Chapter 7: Moving Beyond Foundation Models 147
Introduction 147
Case Study: Visual Q/A 147
Case Study: Reinforcement Learning from Feedback 163
Summary 173
Chapter 8: Advanced Open-Source LLM Fine-Tuning 175
Introduction 175
Example: Anime Genre Multilabel Classification with BERT 176
Example: LaTeX Generation with GPT2 189
Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 193
The Ever-Changing World of Fine-Tuning 206
Summary 207
Chapter 9: Moving LLMs into Production 209
Introduction 209
Deploying Closed-Source LLMs to Production 209
Deploying Open-Source LLMs to Production 210
Summary 225
Part IV: Appendices 227
Appendix A: LLM FAQs 229
Appendix B: LLM Glossary 233
Appendix C: LLM Application Archetypes 239
Index 243
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
