Jonathan Frankle, Chief AI Scientist at Databricks, explains how Databricks built their text-to-image model with full data provenance without web scraping.
“So a lot of models have had Shutterstock data incorporated into them, but this is the only model I know of so far where it was, you know, exclusively and specifically trained just on the vanilla Shutterstock data. There was nothing else mixed in. You know, we didn't go and scrape the web and find other data or combined datasets or anything like that.”
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More from Jonathan Frankle
AssertionNot checkable as stated
Frankle: No Databricks enterprise customer asks for abstract reasoning AI
“I don't think I have a single customer that's asking to, you know, have AI solve abstract reasoning problems.”
Jonathan FrankleJun 25, 2024▶ 1:13:34State of the Art: Training 70B LLMs on 10,000 H100 clusters
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“I think the problems with needle in a haystack are well known. You know, it doesn't measure anything real. You're not even testing the model's ability to holistically use the context just to identify one part of the context. So you can do some wacky things to …”
Jonathan FrankleJun 25, 2024▶ 1:17:50State of the Art: Training 70B LLMs on 10,000 H100 clusters
AssertionSupported
Frankle: Dynamic data mixing during pre-training is effective for domain-specific models
“We've had some surprisingly good luck with this. We just released a paper on it. The details matter a lot and it really matters what you're trying to do with the model. But it's been quite effective for us depending on the setting. And certainly when we're thi…”
Jonathan FrankleJun 25, 2024▶ 53:17State of the Art: Training 70B LLMs on 10,000 H100 clusters
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Frankle: Fault tolerance is missing from fundamental model training primitives
“Fault tolerance is still not really built into any of the fundamental primitives of training models. And so if something breaks, you have to go figure out what broke your job stops. You have to restart your job. It is a nightmare just to get to the point where…”
Jonathan FrankleJun 25, 2024▶ 9:21State of the Art: Training 70B LLMs on 10,000 H100 clusters
AssertionNot checkable as stated
Frankle: Most AI data centers are retrofitted, not built for high heat
“In data centers that for the most part were not built remotely for this kind of power or heat and have been retrofitted for this. Like failures happen on a good day with normal CPUs. And this is not a good day and not a normal CPU for the most part.”
Jonathan FrankleJun 25, 2024▶ 12:41State of the Art: Training 70B LLMs on 10,000 H100 clusters
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Frankle: Large-scale model training forces teams to debug the full infrastructure stack
“It's kind of impossible if you're doing training to not go all the way through the entire stack, regardless of what happens. Like somehow I'm still chatting with cloud providers about power contracts, even though the whole point of dealing with the cloud provi…”
Jonathan FrankleJun 25, 2024▶ 23:07State of the Art: Training 70B LLMs on 10,000 H100 clusters
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