Fine-tune the GPT-3 model using the OpenAI API


I hope this message finds you well. I’m writing to the OpenAI API community forum because I’m trying to fine-tune the GPT-3 model to generate SQL queries, but I’m having some trouble with the process.

Specifically, I’m trying to train the model to generate SELECT statements based on natural language input. I’ve already collected a dataset of natural language questions and corresponding SQL queries, and I’ve created a Python script to fine-tune the GPT-3 model using the OpenAI API.

However, I’m not getting the results I expected. The generated SQL queries are sometimes incorrect or incomplete, and the model seems to struggle with certain types of input questions.

Here are some additional details about my setup:

  • Programming language: Python
  • API endpoint:
  • API key: “■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■IHJHRxQX”

I’m hoping someone in the community can help me troubleshoot the problem or provide some guidance on how to improve the fine-tuning process. Specifically, I’m looking for answers to the following questions:

  • What are some best practices for fine-tuning GPT-3 for SQL generation?
  • Are there any specific hyperparameters that I should adjust for this task?
  • How can I improve the quality and accuracy of the generated SQL queries?

Any suggestions, tips, or advice would be greatly appreciated.

Thank you for your time and assistance.

Best regards,
Chaima Troudi

Based on this guidance, I honestly don’t see how you achieve your goals using fine-tuning:

To me, it sounds like a perfect application for plugins. I’ve not used plugins yet, but I know there is a Zapier plugin and I could see how that could be used to generate SQL commands from AI responses.

A quick Google search turned this up:**strong text**