You can run the quantized model with these steps:
Check requirements from the original. In particular, check python, cuda, and transformers versions.
Make sure that you have installed quantization related packages.

pip install transformers accelerate bitsandbytes>0.37.0

Load & run the model.

python

from transformers import AutoProcessor, AutoModelForVision2Seq
from huggingface_hub import hf_hub_download
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"


model = AutoModelForVision2Seq.from_pretrained('hassenhamdi/granite-vision-3.1-2b-preview-8bit', trust_remote_code=True).to(device)
tokenizer = AutoProcessor.from_pretrained('ibm-granite/granite-vision-3.1-2b-preview')


# prepare image and text prompt, using the appropriate prompt template

img_path = hf_hub_download(repo_id=model_path, filename='example.png')

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": img_path},
            {"type": "text", "text": "What is the highest scoring model on ChartQA and what is its score?"},
        ],
    },
]
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(device)


# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))

Configurations

The configuration info are in config.json.