Google Developer Expert in AI/ML in JAX/FLAX | Docker Captain | Machine Learning Scientist in Quebec

πŸ‡¨πŸ‡¦πŸ‡«πŸ‡·πŸ‡¨πŸ‡΄
Joined May 2010
β€œGeography has made us neighbours. History has made us friends. Economics has made us partners. And necessity has made us allies." "From the fields of Flanders to the streets of Kandahar." Vive la Canada, Team Canada The Maple Leaf Forever
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David Cardozo πŸ‡¨πŸ‡¦ retweeted
if you have a qwen3.5 4b around, please try steering neurons L14 N8949 +1 and L13 N7956 -1 now you have evil qwen recipe: dose Γ— median(β€–residβ€– leaving layer L, tokens 1:) / β€–W_down[:, n]β€–` at the down_proj at every input
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lean.brettkoonce.com/bluepri… My yearly comment of why lean should be a math and CS course
Thanks for the shout out! Lean definitely has a learning curve, but the results can be verified in a way other languages can only dream about!
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Total banger!
WE'D LIKE TO BUY THE WORLD SOME TIME...
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Lean should be part of the math undergrad experience
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David Cardozo πŸ‡¨πŸ‡¦ retweeted
if you have a startup about private LLM inference today is the perfect launch date
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New blog! With @_DivyaMakkar we present a worklog on how to build an asynchronous RL framework from scratch. It covers multi-actor systems in JAX, inference, distributed training, actor coordination, ICI weight sync and more! Additionally we share interesting JAX challenges encountered along the way and how you should think of async RL from a systems perspective!
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I_DECLINE_public_HF_RCE_as_offtask_prodethical
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Simplicity is a virtue, don't try to be too clever
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Just grok: (happy to reproduce this)
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If you want to give credit, either do it like this or don't do it! Joan and Esther, please do a course about hosptitality and how to make people feel welcome.
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David Cardozo πŸ‡¨πŸ‡¦ retweeted
Had the honor of presenting about optimization in flow models at the BCS Department, MIT, and it felt good! Slides: docs.google.com/presentation… YT: youtu.be/okgOtuRUCBs?si=g9Hr… Thanks to the organizers for having me!
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Aritra started the profiling series when we were in Japan, getting addicted to eating Ramen. But from there on, he undertook a rigorous effort to ensure the community becomes comfortable generating and reading profile traces in PyTorch. Almost all the serious practitioners I know of who are working in this space know that profiling is an absolute basic if one wants to understand WTF is going on inside an accelerator. Why is it idle? Why is the model not fast enough? What can be optimized further? However, the chatter around profiling seemed quite quiet, almost as if everyone knew it fully. I think we can agree that it's not the case at all. Hopefully, the three-part blog series takes a good first step at TEACHING you the importance of profiling and how to use it in the grand scheme of things. Rarely does the PyTorch documentation feel insufficient, but it did for profiling. So, I hope this series closes that gap! Get started with the series here: huggingface.co/blog/torch-at…
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our blessed post-training / their barbarous distillation attack
πŸ€– Made with AI
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The final part of the three part "Profiling in PyTorch" series is out now! In Part 1 we profiled addition and multiplication In Part 2 we profiled a torch Linear layer (and also MLP) In this part we profile Attention. > a naive implementation of attention > talk about the torch sdpa API > math backend > xformers backend > flash backend > cuDNN backend PS: This is a long read, bookmark this and read it over the weekends. πŸ€—
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Did I just got insulted? (I got this while pairing with an LLM) Your instinct is actually legally sound in structure β€” it just doesn't survive contact with the evidence.
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Why are we still building LLMs with uniform-width layers? For a decade, we've blindly copied the flat stack from the original Transformer. It turns out, uniform capacity is a massive waste of parameter budget. Another recent paper about non-uniform layers 🧡
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David Cardozo πŸ‡¨πŸ‡¦ retweeted
hello Venezuelan friends, we're very heartbroken about the earthquake. 3 years ago we had a similar earthquake in Turkey where we lost 50.000 souls. to help people out we built a "needs map" where we scraped posts online and put address and name and what the person needs (food, water, saving) so others could help and we managed to get to people. we'd love to help you out, but actually work with local Venezuelan folk in ML, and at HF we are happy to cover inference. please respond to this post if you are interested πŸ™πŸΌ the project is outdated so it would take a bit and you would need people to label a dataset shortly but other than that it should work
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David Cardozo πŸ‡¨πŸ‡¦ retweeted
This slide right here is the reason why I love JAX so much. Call it my bias or whatever, but I absolutely love writing JAX
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