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QuIP#: 2-bit Quantization for LLMs

cornell-relaxml.github.io

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Re: QuIP#: 2-bit Quantization for LLMs

#32
post #27

Just to make sure I'm understanding this correctly. This paper signals that the authors have found a way to run Llama 2 70B, but with 1/8th the VRAM requirements as compared to the original model, right? And the output is on-par with the original along some metrics (ArcE/PiQA), within 25% on others (Wiki/C4), and the trajectory of their progress hints that there's even more ground to gain in the future?

Your last paragraph is the key. Without their improvements, the quality tradeoff would have been hard to stomach.

When considering quality, you should really compare not to the original model, but to smaller models which use the same vram/compute

Re: QuIP#: 2-bit Quantization for LLMs

#33
post #27

Earlier quoted context omitted.

Your last paragraph is the key. Without their improvements, the quality tradeoff would have been hard to stomach.

When considering quality, you should really compare not to the original model, but to smaller models which use the same vram/compute

But people use large model for better metrics, this defeats the purpose.

Re: QuIP#: 2-bit Quantization for LLMs

#37

Does LM Studio support it? By the way, what’s your favorite easy-to-use LLM front end?

LM Studio, sort of. Unfortunately my Macbook is incapable of running all but the smallest models. It would be great if LM Studio can connect to a remote model running on a GPU server that I can rent.

Re: QuIP#: 2-bit Quantization for LLMs

#38

Does LM Studio support it? By the way, what’s your favorite easy-to-use LLM front end?

LM Studio, sort of. Unfortunately my Macbook is incapable of running all but the smallest models. It would be great if LM Studio can connect to a remote model running on a GPU server that I can rent.

You could set up something similar using gradio quite easily:

https://www.jerpint.io/blog/model-inference/

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