Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs 9780138199425
The advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing in recent ye
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English Pages 213 Year 2023
Table of contents :
Title Page
Contents at a Glance
Table of Contents
Preface
Part I: Introduction to Large Language Models
1. Overview of Large Language Models
What Are Large Language Models (LLMs)?
Popular Modern LLMs
Domain-Specific LLMs
Applications of LLMs
Summary
2. Launching an Application with Proprietary Models
Introduction
The Task
Solution Overview
The Components
Putting It All Together
The Cost of Closed-Source
Summary
3. Prompt Engineering with GPT3
Introduction
Prompt Engineering
Working with Prompts Across Models
Building a Q/A bot with ChatGPT
Summary
4. Optimizing LLMs with Customized Fine-Tuning
Introduction
Transfer Learning and Fine-Tuning: A Primer
A Look at the OpenAI Fine-Tuning API
Preparing Custom Examples with the OpenAI CLI
Our First Fine-Tuned LLM!
Case Study 2: Amazon Review Category Classification
Summary
Part II: Getting the most out of LLMs
5. Advanced Prompt Engineering
Introduction
Prompt Injection Attacks
Input/Output Validation
Batch Prompting
Prompt Chaining
Chain of Thought Prompting
Re-visiting Few-shot Learning
Testing and Iterative Prompt Development
Conclusion
6. Customizing Embeddings and Model Architectures
Introduction
Case Study – Building a Recommendation System
Conclusion
7. Moving Beyond Foundation Models
Introduction
Case Study—Visual Q/A
Case Study—Reinforcement Learning from Feedback
Conclusion
8. Fine-Tuning Open-Source LLMs [This content is currently in development.]
9. Deploying Custom LLMs to the Cloud [This content is currently in development.]
Title Page
Contents at a Glance
Table of Contents
Preface
Part I: Introduction to Large Language Models
1. Overview of Large Language Models
What Are Large Language Models (LLMs)?
Popular Modern LLMs
Domain-Specific LLMs
Applications of LLMs
Summary
2. Launching an Application with Proprietary Models
Introduction
The Task
Solution Overview
The Components
Putting It All Together
The Cost of Closed-Source
Summary
3. Prompt Engineering with GPT3
Introduction
Prompt Engineering
Working with Prompts Across Models
Building a Q/A bot with ChatGPT
Summary
4. Optimizing LLMs with Customized Fine-Tuning
Introduction
Transfer Learning and Fine-Tuning: A Primer
A Look at the OpenAI Fine-Tuning API
Preparing Custom Examples with the OpenAI CLI
Our First Fine-Tuned LLM!
Case Study 2: Amazon Review Category Classification
Summary
Part II: Getting the most out of LLMs
5. Advanced Prompt Engineering
Introduction
Prompt Injection Attacks
Input/Output Validation
Batch Prompting
Prompt Chaining
Chain of Thought Prompting
Re-visiting Few-shot Learning
Testing and Iterative Prompt Development
Conclusion
6. Customizing Embeddings and Model Architectures
Introduction
Case Study – Building a Recommendation System
Conclusion
7. Moving Beyond Foundation Models
Introduction
Case Study—Visual Q/A
Case Study—Reinforcement Learning from Feedback
Conclusion
8. Fine-Tuning Open-Source LLMs [This content is currently in development.]
9. Deploying Custom LLMs to the Cloud [This content is currently in development.]
- Author / Uploaded
- Sinan Ozdemir
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- Algorithms and Data Structures: Pattern Recognition
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