Guillaume Lample: belief

Transcript context

…nice. I should also mention for those who are newer to flow matching, I think the creator, this guy’s name is Alex, he’s done I think in Europe’s maybe two Europes as ago. There was, there’s a very good workshop. There’s one hour on like this matching is I would recommend people look that up. That’s the other thing, right? The efficiency wise, like I, I imagine like the reason is open weights the reason you pick 3.6 B backbone it you are 3.4 B you are, try to fit to some kinda hardware constraints. You kinda fits some kinda basic constraints. What are they? Not necessarily, I think something we care about in our model that they’re efficient. So we have a lot of separate model, for instance. So we have this that is very small, very efficient. We also have a small OCR model that is available. Good, highly efficient as well. And I think on a project maybe there, I think companies are going to take is to have a coverage general model that will do a bit of everything. But that is also going to be expensive. On here. What want say is if you care about this specific use case, if you can actually use this model, it just does that. It’s extremely good at it. Survey, very efficient. That’s why we can actually add. We do, but also OCR that are like really good at that. And that would be much more cost effective factors and the general model that will contain a lot of capabilities you don’t really need. So yeah. So we’re doing like general model, but also like more customized model. This, how does it compare to other TTS models? It’s, we are going follow open wave. We’re just dropping it. I think it’s pretty good.…

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