You can get the resulting PPL but that’s only gonna get you a sanity check at best, an ideal world would have something like lmsys’ chat arena and could compare unquantized vs quantized but that doesn’t yet exist

Stop making me want to buy more graphics cards…

Seriously though this is an impressive result, “beating” gpt3.5 is a huge milestone and I love that we’re continuing the trend. Will need to try out a quant of this to see how it does in real world usage. Hope it gets added to the lmsys arena!

If you go for it and need any help lemme know I’ve had good results with Linux and Nvidia lately :)

Btw I know this is old and you may have already figured out your hardware and setup, but p40s and p100s go for super cheap on eBay.

P40 is an amazing $/GB deal, only issue is the fp16 performance is abysmal so you’ll want to run either full fp32 models or use llama.cpp which is able to cast up to that size

The p100 has less VRAM but really good fp16 performance which makes it ideal for exllamav2 usage. I picked up one of each recently, p40 was failed to deliver and p100 was delivered while I’m away, but once I have both on hand I’ll probably post a comparison to my 3090 for interests sake

Also I run all my stuff on Linux (Ubuntu 22.04) with no issues

You shouldn’t need nvlink, I’m wondering if it’s something to do with AWQ since I know that exllamav2 and llama.cpp both support splitting in oobabooga

Yeah q2 logic is definitely a sore point, I’d highly recommend going with Mistral dolphin 2.6 DPO instead, the answers have been very high quality for a 7b model

But good info for anyone wanting to keep up to date on very low bit rate quants!

I don’t have a lot of experience with either at this time, I’ve used them here and there for programming questions but usually I stick to 7b models because I use them for code completion and I only find that useful if it completes the code before I do lol

That said, I’ve had overall good answers from either whenever I’ve decided to pull them out, it feels like wizard coder should be better since it’s so much newer but overall it hasn’t been that different. Wish phind would release an update :(

I run my Nvidia stuff in containers to not have to deal with all the stupid shenanigans

The 3060 is a nice cheap one for running okay sized models, but if you can find a way to stretch for a 3090 or a 7900 XTX you’ll be able to run these 33B models with decent quant levels

First few quants are up: https://huggingface.co/bartowski/WizardCoder-33B-V1.1-exl2

4.25 should fit nicely into 24gb (3090, 4090)

Smaller sizes still being created, 3.5, 3.0, and 2.4

I live in Ontario where we go down to -30C in the harshest conditions.

We have a heat pump and a furnace and they alternate based on efficiency

Somewhere around -5 to +5 C it switches from the heat pump to the furnace

I think you could get by a bit colder but it really loses out on efficiency vs burning gas unless you invest in a geothermal heat pump

Seems relatively uncensored, willing to answer most questions

It’s definitely a little odd… I’m glad they did any kind of official release for 0.2, but yeah information is sorely lacking and would be nice to have more, especially with how revolutionary the previous one was… is this incremental? Is it a huge change? Is it just more fine tuning? Did they start from scratch? We’ll never know 🤷‍♂️

The only concern I had was my god is it a lot of faith to put in this random twitter, hope they never get hacked lol, but otherwise yes it’s a wonderful idea, would be a good feature for huggingface to speed up downloads/uploads

Yeah this seems less focused on creativity, there’s a lot of really good models out there tuned for story telling that will far exceed generalized SoTA models

Better finetuning is such an important factor, i feel like the future is all of us having our own personal tunes for models that work well with our lives, and iterating for learning more basically every day is also really helpful, so the more barriers we can take down the better!

Hmm had interesting results from both of those base models, haven’t tried the combo yet, will start some exllamav2 quants to test

What’s it doing well at?

quant link for anyone who may want: https://huggingface.co/bartowski/OpenHermes-2.5-neural-chat-7b-v3-1-7B-exl2

Btw there’s a 16k tune available now:

https://huggingface.co/bartowski/Orca-2-13B-16k-exl2

I use text-generation-webui mostly. If you’re only using GGUF files (llama.cpp), koboldcpp is a really good option

A lot of it is the automatic prompt formatting, there’s probably like 5-10 specific formats that are used, and using the right one for your model is very important to achieve optimal output. TheBloke usually lists the prompt format in his model card which is handy

Rope and yarn refer to extending the default context of a model through hacky (but functional) methods and probably deserve their own write up

Yeah so those are mixed, definitely not putting each individual weight to 2 bits because as you said that’s very small, i don’t even think it averages out to 2 bits but more like 2.56

You can read some details here on bits per weight: https://huggingface.co/TheBloke/LLaMa-30B-GGML/blob/8c7fb5fb46c53d98ee377f841419f1033a32301d/README.md#explanation-of-the-new-k-quant-methods

Unfortunately this is not the whole story either, as they get further combined with other bits per weight, like q2_k is Q4_K for some of the weights and Q2_K for others, resulting in more like 2.8 bits per weight

Generally speaking you’ll want to use Q4_K_M unless going smaller really benefits you (like you can fit the full thing on GPU)

Also, the bigger the model you have (70B vs 7B) the lower you can go on quantization bits before it degrades to complete garbage