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This is crazy

The llama3 paper is nuts - so many interesting details. Like a table of why training was interrupted on the 24k GPUs. Or predicting benchmark performance prior to training. And data mix.

I suddenly spent 90 minutes going through the paper. This is crazy informative

If you are at ICML and you are interested in AI for molecules and drug discovery, please check out this poster at #ML4LMS workshop today!

The result of 1.5 years of work is out! We've built a corpus covering 100M+ molecules, learned to train 1-2B parameter language models, wrapped LMs into an optimization algorithm, and beaten all benchmarks we've tried. Everything is open-sourced! Please spread the word!

i didn't watch the video, but i agree. that's exactly what we are trying to do at YerevaNN. We showed LMs by themselves do not generate good molecules from the first prompt. But if there is another module that can score the generated molecules, the system becomes interesting!

Yoohoo

The number of anonymous signatories from Google and OpenAI under this letter is more scary than the risks of AI

Agree. A lot of weird problems can be modeled by next token prediction. And we know how to scale transformers to solve them.

Excited to work with Hrayr and YerevaNN team on fighting spurious correlations. This time inside the context of Transformers :)

interesting

This is useful. See you in EMNLP :)

105 poster at EMNLP BlackBoxNLP

I hope some of those images will become publicly available for research use!

What a great email with a slightly spicy endgame: "Good R&D endeavors can do more for progress in fundamental technologies than all the fancy theorizing that we often consider the "real" AI research."

True

So the gap between a frontier model by a major ClosedAI player and an equally powerful open-source model is less than 5 months now...

Okay, Elon knows about Armenia