What does this PR do?

Adds the possibility to load AWQ models if some modules of the model are skipped for quantization.
E.g. for whisper, Llava, Mixtral, we respectively don't want to quantize the encoder, vision encoder and the gate layer to ensure inference stability.

Let's merge it once AWQ makes the 0.1.8 release

cc @ArthurZucker @casper-hansen @TheBloke @SunMarc

casper-hansen/AutoAWQ#248

This PR makes it also possible to run multi-modal models with AWQ:

from transformers import pipeline
from PIL import Image    
import requests

model_id = "ybelkada/llava-1.5-7b-hf-awq"
pipe = pipeline("image-to-text", model=model_id, device=0)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png"

image = Image.open(requests.get(url, stream=True).raw)
prompt = "USER: <image>\nCan you please describe this image?\nASSISTANT:"

outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 100})
print(outputs[0]["generated_text"])

USER: \nCan you please describe this image?\nASSISTANT: The image features a brown and white cat sitting on a green surface, possibly a carpet or a grassy area. The cat is holding a red ball in its paws, seemingly playing with it. The cat appears to be focused on the ball, possibly preparing to play or just enjoying the toy.