Shawn Wang: belief

Transcript context

…Absolutely. I think sort of being this, you know, layer between the raw model and the end application actually buys us a lot in terms of what we can help with. Yeah. Well, you know, they're a bunch of... Security minded people who are trying to offer that as a standalone thing, and it's a feature, not a product. I think I'd agree with that. OpenAI's fine tuning rollout, which was last month, how does that affect HumanLoop? Yeah, so when we started the first version of HumanLoop GPT, 5 wasn't out yet. It was all GPT 3, and we saw a lot of fine tuning at the time. And post the release of... 3. 5 and 4, by virtue of the fact that it was impossible to fine tune, like we could just see it in our analytics. The amount of fine tuning just kind of fell off a cliff partly I think because the models were better. Yeah. But also just partly like it wasn't an option. Yeah. And so I'm kind of interested to see now that 3. fine tuning are back, whether that kind of fully recovers. 4 isn't back yet, but it's, but 3. 5 fine tuning being back we've, we've definitely seen a lot. In the past, people generating outputs with GPT 4, filtering based off evaluation or feedback criteria, and then fine tuning smaller, faster models. And so I think we likely see a lot of fine tuning of GPT 3. 5 on 4 generated data, and that's a workflow that we've been, we natively support within HumanLoop now. So you can actually kind of do all of those things without having to leave it. If you have a bunch of generations, you can filter them on some criteria. Click fine tune, run it ahead of evals, and then decide whether or not to deploy that model. But time will tell as to whether or not this is something that goes back up in importance the way it used to be. Yeah.…

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