Shawn Wang: belief

Transcript context

…Uh, yeah. So. As mentioned, it is highly inefficient. However, I are just showing tons of tokens. As we discover what are the use cases that are truly valuable, we are going to figure out what is the data that was actually valuable through this training process, I think, and we are going to be able to. One, maintain the same large model, but train it more efficiently and quantize it more efficiently and potentially reduce that net required compute. And the other thing is that since we know that this works this well, we can do model distillation. Model distillation is still questionable as whether we can actually get like a Mistral 8 bit to perform similarly as a. Chat GPT or a GPT 4 model in a constraint case, but I think for certain use cases, we'll get there. And for example, if you've seen the Databricks assistant, they do a model college of different types of models for assisting you throughout the process for costs. And also because it just makes sense for certain things, you just need to classify for certain you need to do a full assistant, like level operation and. If you're doing the assistant operation, you don't want to make your SaaS margins go bad because you are now running really intense compute for that element kind of thing. Those are the things that happen behind the scenes. And like Copilot is beloved by people. And people say like, Oh, I just use Copilot. And that's a much smaller model than a GPT 4. I think they've distilled several rounds of OpenAI's original codex model for Copilot, and that seems to make a ton of sense. I was trying to map out the philosophy of distillation, and I've been trying to split out what you distill for. So there's distillation of knowledge, which is what I think people generally think about. But for LLMs, it starts to have also things like distillation of preferences. So like you can sort of use LLMs as judge to basically steal the RLHF capabilities from one model to another model, and then you have the same RLHF. Preference data without paying for it. And then you have distillation of reasoning. I think there's a sort of or orca models where you can kind of put in the like chain of thought into, into the model. I think also like there's a lot of like benchmark gaming. You know, it's well understood that you can distill. Distill the knowledge of the benchmark into a model, and then obviously it's going to perform better on the benchmark. But I think what's less understood now is, um, you know, the sort of un gamable leaderboards, like the LMSys leaderboard, like some, it's also possible to game those things, and you can distill smaller models to do well on those. It's so, with computer vision, we had it gaming the benchmarks all the time. I don't trust benchmarks, especially when the numbers are close. I'm like, okay, this is useless now because it is completely gamified, right? They basically, you just shove the most compute and then you choose the right checkpoint where it magically, mathematically works for the benchmark. Okay. And you choose that, and I had people that were training large models come up to me and tell me, I cannot reproduce this, this is completely unreproducible, but I have the checkpoint, it worked once, we're submitting the paper.…

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