Mixtral

[nivibilla]

Not an issue but seeing that exl2 2 bit quants of a 70b model can fit in a single 24gb GPU. I'm wondering if it's possible to run a quantized version of mixtral 7b*8 on a single 24gb GPU. And if that's something exllama2 could support or a completely different project?

Mistral MoE 7b*8 model https://twitter.com/MistralAI/status/1733150512395038967?t=6jDOugc19MUNyOV1KK6Ing&s=19

[nivibilla]

So far I've heard it requires 2x80gb gpus to run. Or 4x 40gb. Which is just shy of the 140gb needed for 70b fp16. So if it's possible to run a 70b on 24gb then I hope it should be possible to run the MoE on a single 24gb gpu

[turboderp]

I don't doubt that it's possible.

The main challenges are:

1. quantization, specifically, ensuring that the calibration data triggers all of the experts enough, and that you don't end up using a very small sample for an expert that only triggers rarely, and
2. fast batching (and prompt ingestion), since each token in the batch triggers its own pair of experts.

I look forward to some sort of announcement from Mistral. Maybe even some reference code..

[MarkMakers]

So far I've heard it requires 2x80gb gpus to run. Or 4x 40gb. Which is just shy of the 140gb needed for 70b fp16. So if it's possible to run a 70b on 24gb then I hope it should be possible to run the MoE on a single 24gb gpu

It runs on 4 3090's taking under 92gb in 16fp - not sure where these other figures are from. I got it running yesterday.

[ortegaalfredo]

No, it can run in 2x3090 with 8-bit or 4-bit quantization using bitsandbytes, but it runs extremely slow. The only way to make it practical is with exllama or similar. Not even GPTQ works right now.

[nivibilla]

https://twitter.com/4evaBehindSOTA/status/1733551103105720601?t=SiKV8qH1IIoKiQlRBiIR5w&s=19

Tim from bitsandbytes says he's done MoE quantisation before. And that its actually easier than vanilla transformers.

[CyberTimon]

I really hope it get's exllama support. The model is multilingual and seems very powerful. Hope it can be implemented.

The official inference code for vllm etc dropped hours ago.

[CyberTimon]

The mixtral pr got merged into transformers, so you can look at their implementation: https://github.com/huggingface/transformers/pull/27942

Edit: Llama.cpp is now also adding mixtral: https://github.com/ggerganov/llama.cpp/pull/4406

[DutchEllie]

When you do implement it, please don't forget the ROCm implementation 🙇

[neutrino84]

NICE, glad to see this is being talked about 👍

[DogeLord081]

When attempting to use Mixtral-8x7B-v0.1-GPTQ from https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ with chat.py, this error occurs:

python examples/chat.py -m C:\Users\danu0\Downloads\Artificial-Intelligence\Mixtral-8x7B-v0.1-GPTQ -mode raw
 -- Model: C:\Users\danu0\Downloads\Artificial-Intelligence\Mixtral-8x7B-v0.1-GPTQ
 -- Options: ['rope_scale 1.0', 'rope_alpha 1.0']
Traceback (most recent call last):
  File "C:\Users\danu0\Downloads\Artificial-Intelligence\exllamav2\examples\chat.py", line 81, in <module>
    model, tokenizer = model_init.init(args)
  File "C:\Users\danu0\AppData\Local\Programs\Python\Python310\lib\site-packages\exllamav2\model_init.py", line 64, in init
    config.prepare()
  File "C:\Users\danu0\AppData\Local\Programs\Python\Python310\lib\site-packages\exllamav2\config.py", line 133, in prepare
    raise ValueError(f" ## Could not find {prefix}.* in model")
ValueError:  ## Could not find model.layers.0.mlp.down_proj.* in model 

[nivibilla]

It's a different architecture. It won't work out of the box. Pls wait for Turbo lol.

[CyberTimon]

He is working on it. In the experimental branch, there is already a preview. (Which works but is unoptimized)

[turboderp]

All done. For now.