Continuous fine-tuning - Best Practices?

Hi there.

I have gathered a training set for a model, I wish to fine-tune. I have around 80 prompt/completions. Now, I will be gathering training data every week (from my organization), and was wondering if I can “simply” keep fine-tuning my model?

Practically, is it good practice to fine-tune it multiple times? And do I need to keep anything else in mind? (E.g., do I need to include the old/existing training data when re-fine-tuning?)

As far as the docs go:

If you have already fine-tuned a model for your task and now have additional training data that you would like to incorporate, you can continue fine-tuning from the model. This creates a model that has learned from all of the training data without having to re-train from scratch.

To do this, pass in the fine-tuned model name when creating a new fine-tuning job (e.g. -m curie:ft-<org>-<date>). Other training parameters do not have to be changed, however if your new training data is much smaller than your previous training data, you may find it useful to reduce learning_rate_multiplier by a factor of 2 to 4.