Earlier quoted context omitted.
> When you say "fine tune" here what are you looking to do? As an example of fine tuning, I might take a pretrained model and then continue training it with a custom dataset that is tailored to a specific text generation task (not classification). Here is an example of a custom dataset that I might fine tune on: https://github.com/baobabKoodaa/future/blob/8d2ae91e6a6f00c7... I would like the LLM to generate fictional…
I've not yet managed to convince myself if fine tuning LLMs works for that kind of example. Have you tried fine tuning GPT3 via the OpenAI APIs for this? It should only cost a few dollars for that smaller set of examples, and it would at least help demonstrate it it's possible to get the results you want with the current best-in-class language model before you try to run that against a smaller model that you can fit…
I haven't. That's not a bad idea.
> it would at least help demonstrate it it's possible to get the results you want with the current best-in-class language model before you try to run that against a smaller model that you can fit on your own hardware
The dataset you saw was (mostly) generated with ChatGPT and davinci-002, by using prompt engineering instead of fine tuning. So it's definitely possible to produce good results like this (though no judgment here on the question of prompt engineering vs fine tuning).