README.md

---
library_name: transformers
base_model:
- ibm-granite/granite-vision-3.1-2b-preview
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
- granite
- vision
- quantization
---

## About the uploaded model
- Quantized by: hassenhamdi
- Original model: [ibm-granite/granite-vision-3.1-2b-preview](https://huggingface.co/ibm-granite/granite-vision-3.1-2b-preview)
- precision: 4-bit

## Setup
- 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.
```bash
pip install bitsandbytes>=0.39.0
pip install --upgrade accelerate transformers
```

- 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-4bit', 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.