Mikhail Parakhin: evaluation

Transcript context

…Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don’t know why. I’m curious on your explanation. I think you, you, uh, you can make things very approachable. And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid? You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it’s called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities. They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they’re used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it. And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It’s non-transformer architecture that’s more complicated than sta-state space and really difficult to code if you-- if I’m being honest. But it’s, um, very efficient. It’s, uh, subline-- sub, uh, quadratic in, in length of your context. Uh, it’s very compact way to represent things, and that’s a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it’s basically on par with transformers, and if you do hybrids with transformers, it’s, it’s even better. That’s why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels like sometimes even every private model. Uh, but liquid’s been taking quite a bit of, uh, at least internal Shopify share. And the reason I’m excited is, yeah, because it’s, it’s the only non-transformer architecture that I found being genuinely competitive. Uh, and, uh, you know, for we use it for search and for, for long context, uh, pulse distilling and others. This is the overview. I don’t know how approachable Sha, sorry. Maybe, maybe still too obtuse. I, I mean, I think they haven’t been that open about their implementation details. I think the... I would say like liquid hasn’t been like if there’s a lot of technical detail published, I haven’t read like a, a formal sort of paper on the implementation details. Uh, but I, I did get the sort of relationship between the SSMs and the others. This is one of the sort of, uh, charts that was, you know, showing the relationship between like full attention versus Something that’s, uh, more like a RNN type in terms of their, their efficiency. Um, and then the, the other chart was this old one, uh, where it compares versus, uh, some of the other models. Uh, doesn’t exactly have the correct Y-axis, but close enough where you can see like it’s basically a, a step change difference in terms of the efficiency. I think the surprise to me was that you guys are, uh, actively using it already in internally inside of Shopify. And like I, I’m curious, like what are the constraints that you’re optimizing for, right? Is it when you say smaller, is it like the 1B size? Uh, what kind of like latency constraint are you, are you optimizing for? What kind of context length, um, sort of considerations, right? Like I think for example, right, like in the audio kind, kind of use cases, the SSMs ef-effectively have unbounded context length because they, they just have to operate on like the most, the sliding window of the most recent stuff. Uh, I’m just kinda curious, like w-what do you see the potential here?…

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