Convert common context information to all prompts in fine-tuning from Python

I’m using Chatgpt3 to train it on SQL queries.
For now I’m using the “Playground” section where I declare the shape of the db + promp&queries for fine-tuning. Now I’d like to do fine-tune from Python, but the db structure definition isn’t really an example prompt=>response, but it is precisely common to all the examples.
I specify that I need to use Python because I have exceeded the maximum context length of 4097 tokens (if anyone knows how to remove this constraint from Playground, yo are welcome).
How do I communicate the database structure common to all prompts from Python to develop a new specific model?
The most inefficient alternative of all is to repeat the context structure in all prompts, but it is very inefficient and expensive.

What have you tried so far?

Can you show some code? Your prompt? Details help…


Hi @PaulBellow, i have only the prompts from Playground (i have no code for Python):
I’ll give you an example:
Write a query in SQL Server from natural language on the following database:

  • Customer Table (columns: customer name, geographical area, …)
  • Order Table (columns: order date, invoice date, quantity …)
  • List item

    SPECIFIC PART with Query examples (real fine-tuning):
  1. What are the customers? => select customer name from customer table
  2. What are the quantities ordered => select quantity from Orders Table

What settings are you using? Temperature, frequency_penalty, etc.

How are the outputs not what you expected?

Have you searched the forums? I think a few others have tried to do what you’re attempting… I think…