fine-tuning

“So I think there's a strong incentive to make that happen. So the folks who are providing large language models, they want to make their approaches as easy to use as a possible. And so anything that can go into prompting, it seems to me that people will try to…”

“If you have three to five examples, let's say, then I would prompt it. If you have maybe 10 or 50 examples, it would either be prompt tuning or fine tuning. I think generally in that realm, prompt tuning and fine tuning perform similarly, and I would prefer pr…”

“I think you're still going to want to do RAG anyways. Like, even if you have fine tuned models in the mix, RAG is still sort of this, like, last mile data or context.”

“And if you fine-tune a medium-sized-ish model, sometimes it loses the ability to do effective in context learning, because I think the intuition is, it's devoting more, more of its parameter space to, kind of, like, memorizing the training set so it can do bet…”

“We see people experimenting with it. I think the only real place where they're doing it is when they've reached like really critical scale which I still don't think is that many applications to date.”

“So we fine tune on the proprietary data of a particular company, and we can see 10 to 20% improvement on top of the domain specific in fine tuning as well.”

“Fine tuning in many cases doesn't work because you need a lot of data to see the results and there are still hallucinations even after fine tuning.”

“Almost if not all of our customers have moved off of fine-tuned models onto instruction-tuned models and are seeing really good performance.”

“Taking those models and trying to fine tune them It's just, it's not as effective as building it yourself and you have much fewer levers to pull than if you actually have access to the data and you can change the data that goes into that process.”