The OpenAI documentation for the model attribute in the fine-tune API states a bit confusingly:
model
The name of the base model to fine-tune. You can select one of "ada", "babbage", "curie", "davinci", or a fine-tuned model created after 2022-04-21.
My question: is it better to fine-tune a base model or a fine-tuned model?
I created a fine-tune model from ada with file mydata1K.jsonl:
ada + mydata1K.jsonl --> ada:ft-acme-inc-2022-06-25
Now I have a bigger file of samples mydata2K.jsonl that I want to use to improve the fine-tuned model.
In this second round of fine-tuning, is it better to fine-tune ada again or to fine-tune my fine-tuned model ada:ft-acme-inc-2022-06-25? I'm assuming this is possible because my fine tuned model is created after 2022-04-21.
ada + mydata2K.jsonl --> better-model
or
ada:ft-acme-inc-2022-06-25 + mydata2K.jsonl --> even-better-model?