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James Thewlis

@jdthewlis

Chief Scientist & Co-Founder @UnitaryAI | Prev: PhD @Oxford_VGG, MEng @imperialcollege, research w/ @FacebookAI | Computer Vision & AI 🖼🤖
jamesthewlis.com Joined September 2017
550 Following 620 Followers 302 Posts

Latest posts 5

James Thewlis @jdthewlis
I found a subtle axis bug in the MuonH implementation of the Marin 535B training run - but the plot twist is the bug might actually make performance better! And turns out the granularity of Muon matrix grouping is a bit of an open question.

github.com/marin-communit…
github.com MuonH implementation includes the Expert dimension in the frobenius norm computation for 4D inputs... As of #7820 the expert FF parameters are stored in a big [n_layers, n_experts, d_in, d_out] tensor, eg [48, 384, 3072, 3072], with the specialised _newtonschulz_4d_distributed computing Newton Schu...
James Thewlis @jdthewlis
Fun to be able to follow a large training run live! Wanted to understand the arch choices better so had my agents put together a 2-pager.

* Every layer MoE, 8 active + 2 shared
* MuonH optimiser
* Trained 12x past compute-optimal for better intelligence/FLOP at inference
Percy Liang Verified account @percyliang Aug 21
🚢 Marin 535B-A23B started training this week! As usual, the whole process is open.
Voyage plan: pretraining (80%) + midtraining (20%) on 18.75T tokens on 11 x GB200 NVL72 for ~3 months (2.7e24 FLOPs). Post-training will follow.
Before kicking off the run, we trained a 4-rung scaling ladder from 1.6B-A61M (48B tokens) to 27.7B-A1.2B (926B tokens) to debug issues, and to make a forecast of our hero run. This is by far our biggest run, so definitely expecting the unexpected.
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