Speaker unverified: evaluation

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…So that's the skinny thinking. What other went into LlamaTree kind of planning, you know, so LlamaTree, you have a pretty good model. People really liked it. So you drop like the intermediate weight. So it's a 870 and now 405B. What was the thinking behind going so large? I mean, you talked about the hardware capabilities at inference. Like I can now run a 405B model at home for sure. And it might be hard to even get the cloud resources to do it. What was the decision there? The decision is super simple. We want the best model. We want to be number one and number two. We started one year and a half ago and we did quite some journey. We filled the gap with GPT-4. So that will be the first open source model that actually compares to GPT-4. There's now GPT-4o, of course. And we're close, but we're not there yet, not in all capabilities, but the gap is getting smaller and smaller. There's also like what compute we had at the time when we started to run in January. We put a lot of effort there, but as like Mark announced, we have more and more GPUs. So the next generation will be bigger. So that's what drives the decision. Now, maybe let me reflect two things he said. You cannot use it at home. That's probably true, but quantizing it to FP8 can run on Node, even with a long contact of 128K tokens. Second thing is I'm hopeful that the community will lead to a lot of findings by open sourcing it and there is a smart way to actually make you use it on your computer. If you remember Llama 1, Llama 2, like when we published models, people were saying it's too big. And after two weeks, it was running on a Raspberry. I don't know if it will be the same, but I hope it's the same kind of trend. And by releasing those models, we are enabling that. Now, the last thing I want to add is having bigger models enables us to collect better data, for instance, at LHF stage, because that's the model we use for the annotation. And so we distillate straightforward, like this annotation from this better model to the other models. So I can guarantee you that the quality of the smaller models we are releasing with Llama 3 are also thanks to having these artifacts where we can collect and train. Yeah, there's a lot of really good info there. One thing I'll just briefly touch on for quantization. There was a recent Noam Shazir blog post. Noam is writing again for some reason, and he was talking about native FP8 training. It seems like that is most useful for inference. That is what you expect the open source community to do with your weights once you release them anyway. Is there any movement or thinking about just moving to FP8 or whatever other new format is in vogue these days?…

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