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Building AI Applications with ChatGPT APIs

You're reading from  Building AI Applications with ChatGPT APIs

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781805127567
Pages 258 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Martin Yanev Martin Yanev
Profile icon Martin Yanev

Table of Contents (19) Chapters

Preface 1. Part 1:Getting Started with OpenAI APIs
2. Chapter 1: Beginning with the ChatGPT API for NLP Tasks 3. Chapter 2: Building a ChatGPT Clone 4. Part 2: Building Web Applications with the ChatGPT API
5. Chapter 3: Creating and Deploying an AI Code Bug Fixing SaaS Application Using Flask 6. Chapter 4: Integrating the Code Bug Fixer Application with a Payment Service 7. Chapter 5: Quiz Generation App with ChatGPT and Django 8. Part 3: The ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
9. Chapter 6: Language Translation Desktop App with the ChatGPT API and Microsoft Word 10. Chapter 7: Building an Outlook Email Reply Generator 11. Chapter 8: Essay Generation Tool with PyQt and the ChatGPT API 12. Chapter 9: Integrating ChatGPT and DALL-E API: Build End-to-End PowerPoint Presentation Generator 13. Chapter 10: Speech Recognition and Text-to-Speech with the Whisper API 14. Part 4:Advanced Concepts for Powering ChatGPT Apps
15. Chapter 11: Choosing the Right ChatGPT API Model 16. Chapter 12: Fine-Tuning ChatGPT to Create Unique API Models 17. Index 18. Other Books You May Enjoy

Building and Using the Fine-Tuned Model

In this section, we will explore the process of creating and utilizing a fine-tuned model using OpenAI’s CLI. OpenAI offers newcomers the opportunity to avail of a $5 credit to access the ChatGPT API and its fine-tuning services.

Fine-tuning involves building a specialized model based on an existing base model, and in our example, we will use the most advanced ChatGPT model available for fine-tuning called davinci. We will improve the performance of that model for book summarization tasks.

We will learn how to start a fine-tuning job, which uploads and processes the training data, and then we’ll monitor its progress until completion. Once the fine-tuning job is done, we will use the newly created fine-tuned model to generate text. We’ll learn how to make requests to the fine-tuned model using the completions API, and we’ll cover how to manage and delete fine-tuned models if needed.

We will begin by using our...

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