Will a LLM trained with FP4 have competitive performance in 2 years time?
16
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Jan 21
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"Currently, the technology for 4-bit training does not exists, but research looks promising and I expect the first high performance FP4 Large Language Model (LLM) with competitive predictive performance to be trained in 1-2 years time." (see: https://timdettmers.com/2023/01/16/which-gpu-for-deep-learning/)

Granted, the model must be open source for us to know, so the market will resolve based on publicly available information.

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@typedfemale Hi! Would you mind resolving this market?

@Gabrielle @Bayesian we got a lesson about this in Discord, here

My executive summary:

  • There's some evidence that FP4 is 'around the corner' and may demonstrate some of these qualities

But it's not enough to qualify for the market's criteria of:

  • Open source

  • Publicly available information

  • As of 21 January 2025

If someone disagrees with the way I'm spinning the summary of the conversation, post here!

@Manifold

@mods

@ManifoldAI

Any AI expert can chime in and resolve this market? According to a prompt on chatGPT that I made, this should resolve no

predictedNO

Exclusively in FP4? Or does partially in FP4 count. What if the model is on average 60% FP4 over the course of training?

I guess you covered this with "trained in 4-bit (to some extent)"

predictedNO

@NoaNabeshima This is ab post-training precision adjustments

Competitive with what? SOTA with fp16?

predictedNO

This seems important @typedfemale
Will this resolve YES if scaling laws suggest a 4-bit model would be competitive if compute-matched to a SOTA 16-bit model?

predictedNO

(but there isn't a trained SOTA 4-bit model)

@NoaNabeshima Yes, you need to be better than everything else, but be trained in 4-bit (to some extent)

@typedfemale Finetuned w 4 bit would trigger Yes? 80% of parameters in 4 bit would trigger Yes?

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