LocalLLaMA

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[Paper] The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits (huggingface.co)

submitted 1 year ago* (last edited 1 year ago) by [email protected] to c/[email protected]

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From the abstract: "Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}."

Would allow larger models with limited resources. However, this isn't a quantization method you can convert models to after the fact, Seems models need to be trained from scratch this way, and to this point they only went as far as 3B parameters. The paper isn't that long and seems they didn't release the models. It builds on the BitNet paper from October 2023.

"the matrix multiplication of BitNet only involves integer addition, which saves orders of energy cost for LLMs." (no floating point matrix multiplication necessary)

"1-bit LLMs have a much lower memory footprint from both a capacity and bandwidth standpoint"

Edit: Update: additional FAQ published

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[–] [email protected] 2 points 1 year ago* (last edited 1 year ago) (1 children)

I think we're already getting there. Lots of newer phones include AI accelerators. And all the companies advertise for AI. I don't think they're made to run LLMs, but anyways. Llama.cpp already runs on phones. And the limiting factor seems to be the RAM. I've tried Microsoft's "phi-2", quantized and on slow hardware, it's surprisingly capable for such a small model. Something like a ternary model would significantly cut down on the amount of RAM that is being used which allows to load larger models while also making it faster, everywhere. So I'd say yes. And it would also allow me to load a more intelligent model on my PC.

I think the doing away with matrix multiplications is also a big deal, but has little consequences as of today. You'd first need to re-design the chips to take advantage of that. And local inference is typically limited by memory bandwidth, not multiplication speed. At least as far as I understand.

I'd say if this is true, it allows for a big improvement in parameter count for all kinds if use-cases. But I've also come to the conclusion that there might be a caveat to that. Maybe the training is prohibitively expensive. I don't really know, at this point there is too much speculation going on and I'm not really an expert.

[–] [email protected] 2 points 1 year ago

Yeah I knew about the AI chips being more common but this is a really good write up, thanks!