Shawn Wang: belief

Transcript context

…so like PHY 1.5 has never read Wikipedia, for example, so it doesn't know who Tom Cruise is, you know, it doesn't know who anybody is, it doesn't know about any movies, it doesn't really know anything about anything, like, because it's never read anything, you know, it was trained on a nearly entirely synthetic data set, which is designed for it to learn reasoning, and so it was a research project, and a really good one, and it definitely shows us a powerful direction in terms of what you can do with synthetic data, and wow, gosh, even these tiny models can get pretty good reasoning skills, pretty good math skills, pretty good coding skills, but I don't know if it's a model you could necessarily build on. Some people have tried to do some fine tunes of it, and again, they're like surprisingly good in some ways for a 1.5b model, but not sure you'd find it useful for anything. I think that's the struggle of pitching small models, because small is great, you know, you don't need a lot of resources to run them, but the performance evaluation is always so iffy, it's always just like, yeah, it works on some things, and we don't trust it for others. Yeah, so that's why we're back to fine tuning. So Microsoft did create a 5.1.5 web, but they didn't release it, unfortunately. I would say a 5.1.5 web with fine tuning for your task, you know, might quite, you know, might solve a lot of tasks that people have in their kind of day-to-day lives. You know, particularly in kind of an enterprise setting, I think there's a lot of like repetitive kind of processing that has to be done. It's a useful thing for coders to know about, because I think quite often you can like replace some thousands and thousands of lines of complex buggy code, maybe with a fine tune, you know.…

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