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Building AI Applications with ChatGPT APIs

You're reading from   Building AI Applications with ChatGPT APIs Master ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects

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Product type Paperback
Published in Sep 2023
Publisher
ISBN-13 9781805127567
Length 258 pages
Edition 1st Edition
Tools
Concepts
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Author (1):
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Martin Yanev Martin Yanev
Author Profile Icon Martin Yanev
Martin Yanev
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with OpenAI APIs FREE CHAPTER
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

Fine-Tuned Model Dataset Preparation

To effectively fine-tune our model, we need to prepare the training data in a specific format. In this section, we will walk you through the process of data preparation using a JSON file and the OpenAI CLI data preparations tool.

When preparing data for a fine-tuned model such as OpenAI’s, it’s essential to follow a structured process to ensure optimal performance and accurate results. The first step is to gather the relevant data that will be used to train the model. This data can come from a variety of sources, such as books, articles, or even specialized datasets.

To begin, create a new folder called Fine_Tune_Data on your desktop, and inside the folder, create a new file called train_data.json. For our book summary fine-tuned model, we will use one-sentence summaries for 30 different books. Those summaries will be written inside the file we just created in a JSON format:

[
{"prompt": "Book Summary: The Adventure...
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