I've gotten hyper-conscious to the word "delve" now. I now see it everywhere and i cant help but assume someone used GPT to write or rephrase whatever it is I'm reading.
@soumithchintala
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Foundation Engineers Hard Replace Large Tech Restructuring
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hmmm, so its one of those location refactors (quite sad, but classic large-company stuff).
I've generally found that Foundation / Core language engineers are pretty hard to replace or be fungible — they have deep knowledge about complex code and social dynamics that are hard to -
Google Lays Off Entire Python Foundations Team
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apparently Google laid off their entire Python Foundations team, WTF!
( @SkyLi0n who is one of the pybind11 maintainers just informed me, asking what ways they can re-fund pybind11)
The team seems to have done substantial work that seems critical for Google internally as well. -
SIMD and Parallelization Optimization Challenges
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yea that and other SIMD / parallelization unoptimizations
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PyTorch API design constraints for computational efficiency
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pytorch has a history of constraining API to be computationally efficient (in eager), in a "dont let user shoot themselves in the foot" kinda way. this is one of those constraints.
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Gemini 1.5 Pro: Model size specifications and capabilities
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what do you mean by "it's class"?
Did Gemini 1.5 Pro's size ever get revealed? -
Microsoft Phi-3 Model Shows Impressive Results
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nice work on Phi-3 @SebastienBubeck and team 🙂
— Soumith Chintala (@soumithchintala) 23 avril 2024
results look really impressive. https://t.co/6sLVl478G5nice work on Phi-3 @SebastienBubeck and team 🙂
results look really impressive. -
Llama3-70B Achieves Top Ranking on English-only Benchmark
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also, Llama3-70B is #1 on English-only, whut!!!!
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Llama3-70B ranks fifth as 405B model approaches release
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Llama3-70B has settled at #5. With 405B still to come next… I remember when GPT-4 released in March 2023, it looked like it was nearly-impossible to get to the same performance. Since then, I've seen @Ahmad_Al_Dahle and the rest of the GenAI org in a chaotic rise to focus,
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CUDA CuDNN Python Startup Costs and Compilation Overhead
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its unclear to me even for code because compile-time and startup costs (even for a CUDA/CuDNN loading Python program) is 1 second.
If you strip out the binary blobs that need to load (say business logic filled program), then its quicker, but i'm not sure if there aren't other