This is a case where I kind of disagree. The way one would normally fine-tune, we want AI to infer, inference being the ability to come up with new answers that were never in the training set, but are only based on learning behaviors. Filling in the gaps in fine-tune from its corpus knowledge.
Here, you really want a “canned answer machine”, that can only give you back what you’ve put into the training file for responses. Overfitting, if it was any other AI language application.
There are other hyperparameters besides “epochs” now exposed. They are not seen in the slick GUI either to be specified or recalled, you have to use API calls. learning_rate_multiplier for example, you can reduce your token cost by increasing that instead of increasing epochs. 2.0 has been seen used by the auto settings on smaller training sets, and you can pull down what was used on your own “auto” job. And continuing on an existing fine-tune, if you want to see the improvement or degradation of running more passes of learning.
Here’s another very similar thread today, where you can explore to come up with some ideas, because fine-tune is very much charting your own course, with sparse guidance from OpenAI, and your own experimentation needed: