3 ms·
That's an obvious exaggeration. The competition is using smaller weights already, some of which are floating point and some of which aren't. And they use full
by Dylan16807 1y ago
That's an obvious exaggeration. The competition is using smaller weights already, some of which are floating point and some of which aren't.
And they use full size floats for training.
- imtringued 1y agoThat means their paper is actually worse than SOTA, which is concerned with training in fp4 natively without full precision [0] for QAT. [0] "full precision" in ML usually means 16 bit floats like bfloat16
- Dylan16807 1y agoI wouldn't say "worse". It's focusing on inference cost and leaving training at a default for now.