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Wow - This seems at first read to be really impressive work. They got scaling laws up to a reasonable size, 2.7B, and also run a few downstream tasks. Would be
by buildbot 2y ago
Wow - This seems at first read to be really impressive work. They got scaling laws up to a reasonable size, 2.7B, and also run a few downstream tasks. Would be interesting to see how a comparable model trained by someone else does, to check their scores against those.
They get real (61%!?) memory savings during training, and inference too.
On top of all that, they then go build an FPGA core which is programmed with a custom assembler. And their code is posted and works seamlessly with huggingface transformers?! Absolutely going to test this out.
- kgc 2y agoPlease let us know how the tests go!
- buildbot 2y agoSo far pretty awesome - simpler first pass, integrated their BitLinear layer into my training setup, and got a run with just the MLPs swapped to see how things are- at comparable parameter counts (3.2B), the BitLinear MLP model is slightly lower loss!
- sitkack 2y agoIt doesn't look like the FPGA portion of the artifact is released.