LLM Book 9 - Deployment, Inference, and Model Compression with Large Language Models for Entry-level

Table of Contents

1. Introduction

1.1 What are Large Language Models?

1.2 Why are they Useful and Challenging?

1.3 What are the Main Goals and Scope of this Book?

2. Deployment of Large Language Models

2.1 Overview of Deployment Options and Platforms

2.2 How to Deploy Large Language Models on Cloud Services

2.3 How to Deploy Large Language Models on Edge Devices

2.4 How to Monitor and Manage Deployed Models

3. Inference with Large Language Models

3.1 Overview of Inference Tasks and Scenarios

3.2 How to Perform Inference with Large Language Models using APIs

3.3 How to Perform Inference with Large Language Models using Libraries 3.4 How to Optimize Inference Performance and Cost

4. Model Compression of Large Language Models

4.1 Overview of Model Compression Techniques and Benefits

4.2 How to Apply Quantization to Large Language Models

4.3 How to Apply Pruning to Large Language Models

4.4 How to Apply Distillation to Large Language Models

5. Conclusion

5.1 Summary of the Main Points and Lessons Learned

5.2 Future Trends and Directions for Large Language Models 5.3 Resources and References for Further Learning