Can't wait for the 1 bit quantization

Wait, the https://huggingface.co/decapoda-research/llama-13b-hf-int4/resolve/main/llama-13b-4bit.pt is the Facebook one ?

Is it fully open now ?

It's the HuggingFace transformers module version of the weights from Meta/Facebook Research.

https://github.com/huggingface/transformers/pull/21955

I have to ask… is this a joke or are people actually working on digitizing trained networks?

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Check out the terms "quantization aware training" and "post training quantization".

8-bit, 4-bit, 2-bit, hell even 1-bit inference are scenarios which are extremely relevant for edge devices.

><9 GiB VRAM

So does that mean my 1060 6GB can run it....? haha.

I doubt it, but I'll give it a shot later just in case.

Isn't 1-bit quantisation qualitatively different as you can do optimizations only available if the parameters are fully binary?

Should work fine with the 7b param model: https://huggingface.co/decapoda-research/llama-7b-hf-int4

Anyone evaluate all the quantized versions and compare them against smaller models yet? How many bits can you throw away before you're better of picking a smaller version?

Wait until you hear about the 1/2 bit.

Super neat. Thanks for the reply. I'll try that.

Also, do you know if there's a local interface for it....?

I know it's not quite the scope of the post, but it'd be neat to interact with it through a simple python interface (or something like how Gradio is used for A1111's Stable Diffusion) rather than piping it all through Discord.

There is a repo for CPU interference written in pure C++: https://github.com/ggerganov/llama.cpp

30B model can run on just over 20GB of RAM and take 1.2sec per token on my i7 8750H. Though actual Windows support has yet to arrive and as of right now the output is garbage for some reason.

Edit: fp16 version works. It's 4 bit quantisation that returns garbage.

There's an inference engine class if you want to build out your own API:

https://github.com/AmericanPresidentJimmyCarter/yal-discord-bot/blob/main/bot/llama_model/engine.py#L56-L96

And there's a simple text inference script here:

https://github.com/AmericanPresidentJimmyCarter/yal-discord-bot/blob/main/bot/llama_model/llama_inference.py

Or in the original repo:

https://github.com/qwopqwop200/GPTQ-for-LLaMa

BUT someone has already made a webUI like the automatic1111 one!

https://github.com/oobabooga/text-generation-webui

Unfortunately it looked really complicated for me to set up with 4-bits weights and I tend to do everything over a Linux terminal. :P

https://github.com/qwopqwop200/GPTQ-for-LLaMa

Performance is quite good.

Most excellent. Thank you so much! I will look into all of these.

Guess I know what I'm doing for the rest of the day. Time to make more coffee! haha.

You are my new favorite person this week.

Also, one final question, if you will. What's so unique about the 4-bit weights and why would you prefer to run it in that manner? Is it just VRAM optimization requirements....? I'm decently versed in Stable Diffusion, but LLMs are fairly new territory for me.

My question seemed to have been answered here, and it is a VRAM limitation. Also, that last link seems to support 4-bit models as well. Doesn't seem too bad to set up.... Though I installed A1111 when it first came out, so I learned through the garbage of that. Lol. I was wrong. Oh so wrong. haha.

Yet again, thank you for your time and have a wonderful rest of your day. <3

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I'm running it using https://github.com/ggerganov/llama.cpp. The 4-bit version of 13b runs ok without GPU acceleration.

Nice!

How's the generation speed...?

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It takes about 7 seconds to generate a full response using 13B to a prompt with the default (128) number of predicted tokens.

only if you turn your pc case upside down

> BUT someone has already made a webUI like the automatic1111 one!

There's a subreddit for it over at /r/Oobabooga too that deserves more attention. I've only had a little time to play around with it but it's a pretty sleek system from what I've seen.

> it looked really complicated for me to set up with 4-bits weights

I'd like to say that the warnings make it more intimidating than it really is. I think it was just copying and pasting four or five lines for me onto a terminal. Then again I also couldn't get it to work so I might be doing something wrong. I'm guessing it's just that my weirdo gpu wasn't really accounted for somewhere. I'm going to bang my head against it when I've got time just because it's frustrating having tons of vram to spare and not getting the most out of it.

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You could pack more bits in your bit with in-memory compression. You'd need hardware support for decompression inside the processor core.

Or make it data free altogether

https://github.com/philipl/pifs

I'm having an issue with the C++ compiler on the last step.

I've been trying to use python 3.10.9 though, so maybe that's my problem....? My venv is set up correctly as well.

Not specifically looking for help.

Apparently this person posted a guide on it in that subreddit. Will report back if I am successful.

edit - Success! But, using WSL instead of Windows (because that was a freaking headache). WSL worked the first time following the instructions on the GitHub page. Would highly recommend using WSL to install it instead of trying to force Windows to figure it out.

r/Oobabooga isn't accessible for me.

Wtf is that GitHub handle lol

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That is slowwwww

It got leaked, not officially released. I have 30B 4 bit running here.

oooh, I've been getting trash responses from opt-6.7b hopefully this is better.

Guess hospice is pretty boring

It is. But that doesn't mean 1-bit neural nets are impossible. Even Turing himself toyed with such networks – https://www.npl.co.uk/getattachment/about-us/History/Famous-faces/Alan-Turing/80916595-Intelligent-Machinery.pdf?lang=en-GB

Where can I see stuff generated from this model?

That is fast. We are literally talking about a high end laptop CPU from 5 years ago running a 30B LLM.

Anyone having any luck finetuning LLama in a multi-gpu setup?

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I'm not actually sure. I've just been chatting with people in an unrelated Discord's off topic channel about it.

I'd post some of what I've got from it but I have no idea what I'm doing with it and don't think what I'm getting would be decently representative of what it can actually do.

They managed to run the 7B model on a Raspberry PI and a Samsung Galaxy S22 Ultra.

Oh, definitely, it's an amazing optimization.

But less than a token a second is going to be too slow for a lot of real time applications like human chat.

Still, very cool though

Does it run on a RTX 3090 ?

It should, yea. I'm running it on a 4090 which has the same amount of VRAM. It takes about 20-21 GB of RAM.

Cool, it is sad here is no download link to try it 🙂

Depends on the model. Some have difficulty even with full 8bit quantization; others you can go to 4bit relatively easily. There is some research that suggests 3bit might be the useful limit, with rarely certain 2bit models.

I imagine 1 token per 0.2 seconds would be fast enough. That'd be equivalent to a 60 WPM typer.

Someone should benchmark it on an AMD 7950X3D or Intel 13900-KS

yeah theres definitely a threshold in there where its fast enough for human interaction. It's only an order of magnitude off, that's not too bad.

Already heared about this project? https://github.com/ggerganov/llama.cpp -> It's very fast!!

>Samsung Galaxy S22 Ultra.

can you link to the samsung galaxy post? that sounds great