I understand the obsession with low bit quantization, but it is empirically quite evident that it is not possible to compress models to less than 4 bit per weight without severe loss of capabilities².
It may be nice as an experiment, but it is obviously a very inefficient route for model training: spending all the flops on a saturated model only to prune its capabilties.
²As to why, I have seen few explanations. But the empirical evidence is there.
Perf goes from 80% to 47% on Wikitext-2. Also no comparisons to FP4 solutions that are able to maintain or exceed perf on the same dataset 80% perf with a 4.25-4.5 big budget.
I think more meaningful thing here would be a hybrid solution that went down to sub-bit representations when the informational representation does not need it (for example later layers) that still maintains task performance
I don't think it's trying to be a useful implementation, but the significance they do provide is that they are able to improve on the relative loss at lower quants.
So, not something anyone would want to run currently, but an indicator that there is still more to squeeze out of lower precisions.
Trellis quantization is a far more approachable enhancement right now, but it doesn't cross the 1-bit barrier (and perhaps doesn't intend to).
I understand the obsession with low bit quantization, but it is empirically quite evident that it is not possible to compress models to less than 4 bit per weight without severe loss of capabilities².
It may be nice as an experiment, but it is obviously a very inefficient route for model training: spending all the flops on a saturated model only to prune its capabilties.
²As to why, I have seen few explanations. But the empirical evidence is there.
Perf goes from 80% to 47% on Wikitext-2. Also no comparisons to FP4 solutions that are able to maintain or exceed perf on the same dataset 80% perf with a 4.25-4.5 big budget.
I think more meaningful thing here would be a hybrid solution that went down to sub-bit representations when the informational representation does not need it (for example later layers) that still maintains task performance
Can this produce a useful model? So far 1 bit quants have been less useful than smaller models that use the same memory
I don't think it's trying to be a useful implementation, but the significance they do provide is that they are able to improve on the relative loss at lower quants.
So, not something anyone would want to run currently, but an indicator that there is still more to squeeze out of lower precisions.
Trellis quantization is a far more approachable enhancement right now, but it doesn't cross the 1-bit barrier (and perhaps doesn't intend to).
I recently found this 1.58-bit model for ASR and it's surprising good (and very fast), that being said it's not a LLM.
https://huggingface.co/moondream/parakeet-redux
Their paper shows this comes with huge quality loss, but that doesn't make it a negative result by any means
Thought this was going to be on the original Little Bit paper, always nice to find out about a surprise sequel!
Has anybody tested this? Are there any available computed models to test?
Has anyone tried this on apple silicon M1-5? Any benchmarks/comps?