Post
3943
After training ððĶðĻðĨððð on ððð ðððððŽ for nearly a month, I've come to realize something most people overlook: ðĒð§ððŦððŽððŦðŪðððŪðŦð ðĒðŽ ððĄð ðĶððĪð-ðĻðŦ-ððŦðððĪ ðððððĻðŦ ðĒð§ ððð ððŦððĒð§ðĒð§ð . ðĨ
Everyone talks about model architecture and data quality. And yes, those matter immensely. But here's what nobody tells you: when your training run fails at 2 AM because of mysterious ðððð ððŦðŦðĻðŦðŽ, or when your expensive GPU cluster is running at ðð% ððððĒððĒðð§ððē, the problem isn't your model. It's most probably a ðĶðĒðŽðŪðŽð ðĻð ððĄð ðĄððŦðð°ððŦð. ð ïļ
Questions that seemed simple but had no clear answers: Why is ððĻð ððŦððĒð§ðĒð§ð ðŽðĨðĻð°ððŦ ððĄðð§ ððð§ðŽð ðĶðĻðððĨðŽ? Which ðððð ððĨðð ðŽ should we actually set? How often should we checkpoint without killing throughput?
That's why we built ððĄð ððĶðĻðĨ ððŦððĒð§ðĒð§ð ððĨððēððĻðĻðĪ ð: a complete guide covering everything from model architecture and data curation to the SmolLM3 training marathon, post-training techniques, and crucially, the ðĒð§ððŦððŽððŦðŪðððŪðŦð ðĨððēððŦ that most teams get wrong.
We validated real vs theoretical bandwidth across the entire stack: ðððð ðĄðĒðððĒð§ð ð ðð/ðŽ, ððððĒð§ðĪ ð.ð ðŦððððĄðĒð§ð ððð ðð/ðŽ, ðððð ððð§ð ðð ðð.ð ðð/ðŽ. Then we ran collective operations across ððð ððððŽ (16 nodes, 8xH100s each) and measured how performance degrades at scale: all-reduce drops from ððð ðð/ðŽ on a single node to ððð-ððð ðð/ðŽ across 16 nodes.
If you've ever wondered why your training runs are slower than they should be, or you're planning to scale up and want to avoid expensive mistakes, this guide might save you weeks of debugging.
ððĄð ððĶðĻðĨ ððŦððĒð§ðĒð§ð ððĨððēððĻðĻðĪ: https://lnkd.in/e5MKXUHS
Shared with âĪïļ by the HuggingFace team
Everyone talks about model architecture and data quality. And yes, those matter immensely. But here's what nobody tells you: when your training run fails at 2 AM because of mysterious ðððð ððŦðŦðĻðŦðŽ, or when your expensive GPU cluster is running at ðð% ððððĒððĒðð§ððē, the problem isn't your model. It's most probably a ðĶðĒðŽðŪðŽð ðĻð ððĄð ðĄððŦðð°ððŦð. ð ïļ
Questions that seemed simple but had no clear answers: Why is ððĻð ððŦððĒð§ðĒð§ð ðŽðĨðĻð°ððŦ ððĄðð§ ððð§ðŽð ðĶðĻðððĨðŽ? Which ðððð ððĨðð ðŽ should we actually set? How often should we checkpoint without killing throughput?
That's why we built ððĄð ððĶðĻðĨ ððŦððĒð§ðĒð§ð ððĨððēððĻðĻðĪ ð: a complete guide covering everything from model architecture and data curation to the SmolLM3 training marathon, post-training techniques, and crucially, the ðĒð§ððŦððŽððŦðŪðððŪðŦð ðĨððēððŦ that most teams get wrong.
We validated real vs theoretical bandwidth across the entire stack: ðððð ðĄðĒðððĒð§ð ð ðð/ðŽ, ððððĒð§ðĪ ð.ð ðŦððððĄðĒð§ð ððð ðð/ðŽ, ðððð ððð§ð ðð ðð.ð ðð/ðŽ. Then we ran collective operations across ððð ððððŽ (16 nodes, 8xH100s each) and measured how performance degrades at scale: all-reduce drops from ððð ðð/ðŽ on a single node to ððð-ððð ðð/ðŽ across 16 nodes.
If you've ever wondered why your training runs are slower than they should be, or you're planning to scale up and want to avoid expensive mistakes, this guide might save you weeks of debugging.
ððĄð ððĶðĻðĨ ððŦððĒð§ðĒð§ð ððĨððēððĻðĻðĪ: https://lnkd.in/e5MKXUHS
Shared with âĪïļ by the HuggingFace team
