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Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen...

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Sam Rodriques

21,489 subscribers

24,338 Aufrufe • vor 6 Monaten •via X (Twitter)

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7 Kommentare

Profilbild von Sam Rodriques
Sam Rodriquesvor 6 Monaten

See our full blog post here:

Profilbild von Anshul Kundaje
Anshul Kundajevor 6 Monaten

👏👏👏👏👏 hurrah for open source!!

Profilbild von Harry
Harryvor 6 Monaten

🇺🇸

Profilbild von Jason Kelly
Jason Kellyvor 6 Monaten

Congrats Sam !! 🎉🎉🎉

Profilbild von Millennium Twain #Truth&NonViolence
Millennium Twain #Truth&NonViolencevor 6 Monaten

End of State-programmed Math, Physics, Technology Fraud, Ignorance!

Profilbild von Mario.dc
Mario.dcvor 5 Monaten

Exciting developments, Sam! Collaborations like these can shape the future of research and competitiveness. The open-source model has the potential to revolutionize how knowledge is shared and applied. Looking forward to seeing the impact! 🤝

Profilbild von Vincent Weisser
Vincent Weisservor 6 Monaten

congrats!! excited for the scientific agents you are building

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learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

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