Shawn Wang: uncertainty

Transcript context

…they are. Oh yes, yes, sorry. And so I will put things in my words and you can correct me as, as, as my I'll be the village idiot here. So, so fine tuning is basically sort of grabbing an open source model off the shelf, and then basically doing further training on it with a custom dataset of your own. Primarily, people use it, think about it as fine tuning for JSON output, or fine tuning for a style of response. Let's say you wanted to tell jokes, or be funny, or be short, or whatever. Just the open source AI community has really fine tuned in all sorts of different manner. I think we'll go over those those things now. Let's go over those things now, and then we'll talk about fine tuning methods. So there's a universe of people who fine tune stuff. Yesterday in your slides, you had, I'll just list some of these and then we'll maybe go through some of them, right? So Technium is personally leading Open Hermes, which is I think the sort of premier model out of the news. news community. There's OpenOrca, which you had a hand in. News, the news research itself also has Capybara and Puffin and all the others. There's Pygmalion, which I've never messed with. Eric Hartford, I am aware of his Uncensored Models and his Samantha Models. Disco Research with Disco LM. And then you personally have done Manticore, Minotaur, Jackalope, and Hippogriff. What should people know about all these names? Being part of AI Twitter is seeing all these things and going dude, I'm being DDoS'ed by all these things and I don't know how different they are. What should people know? Yeah, so I think on a lot of these models, generally, we like to think of those as sort of general models, so If you think about it, what is GPT 4, what is Chad GPT? It's a good general model, and then, One of the services I think that OpenAI offers is like these fine tunings where you're a business and you have very specific business use cases and you might fine tune for that use case. All of these models are really just general use case that you can then go and maybe Fine tune another lore over it for your use cases, but they tend to be good. With good being relative, it's open source. Open source AI is still sort of is infancy. So, good is, it's pretty reasonable. It's probably still better than most, high schoolers at answering questions and being able to like figure things out and, and reasoning skills and math and those sorts of things, right?…

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