> Fine-tuning is good for treating it how to act, but not great for reciting/recalling data.
What underlying process makes it this way? Is it because the prompt has heavier weight?
What underlying process makes it this way? Is it because the prompt has heavier weight?
Fine-tuning is like having the model take a class on a certain subject. By the end of the class, it's going to have a general understanding on how to do that thing, but it's probably going to struggle when trying to quote the textbooks verbatim.
A good use-case for fine-tuning is teaching it a response style or format. If you fine-tune a model to only respond in JSON, then you no longer need to include formatting instructions in your prompt to get a JSON output.
W = W0 + B A
Where W0 is the trained model’s weights, which are kept fixed, and A and B are matrices but with a much much lower rank than the originals (say r = 4).
It has been shown (as mentioned in the lora paper that training for specific tasks results in low rank corrections, so this is what it is all about. I think that doing LoRa can be done locally.