LLM Book 7 - Fine-Tuning, Transfer Learning, and Prompt Engineering for Large Language Models for Rookies
Table of Contents
1. Introduction
1.1 What are large language models and why are they useful?
1.2 What are the challenges and limitations of large language models?
1.3 What are the main goals and methods of this book?
1.4 How to use this book and what are the prerequisites?
2. Fine-Tuning
2.1 What is fine-tuning and when to use it?
2.2 How to fine-tune a large language model for a specific task or domain?
2.3 What are the best practices and tips for fine-tuning?
2.4 What are the common pitfalls and challenges of fine-tuning?
2.5 How to evaluate and compare the performance of fine-tuned models?
3. Transfer Learning
3.1 What is transfer learning and when to use it?
3.2 How to transfer the knowledge of a large language model to a new task or domain? 3.3 What are the different types and levels of transfer learning?
3.4 What are the best practices and tips for transfer learning?
3.5 What are the common pitfalls and challenges of transfer learning?
3.6 How to evaluate and compare the performance of transfer learning models?
4. Prompt Engineering
4.1 What is prompt engineering and when to use it?
4.2 How to design and optimize prompts for a large language model?
4.3 What are the different types and components of prompts?
4.4 What are the best practices and tips for prompt engineering?
4.5 What are the common pitfalls and challenges of prompt engineering?
4.6 How to evaluate and compare the performance of prompt engineering models?
5. Conclusion
5.1 What are the main takeaways and lessons learned from this book?
5.2 What are the current trends and future directions of large language model adaptation? 5.3 What are the ethical and social implications of large language model adaptation?
5.4 How to stay updated and learn more about large language model adaptation?
