Create readme.md

readme.md

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+---
+license: mit
+---
+
+The model corresponds to [Compare2Score](https://compare2score.github.io/).
+
+## Quick Start with AutoModel
+
+<!-- For this image, ![](https://raw.githubusercontent.com/Q-Future/Q-Align/main/fig/singapore_flyer.jpg) start an AutoModel scorer with `transformers==4.36.1`:
+ -->
+```python
+import requests
+import torch
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("q-future/Compare2Score", trust_remote_code=True, attn_implementation="eager", 
+                                             torch_dtype=torch.float16, device_map="auto")
+
+from PIL import Image
+image_path_url = "https://raw.githubusercontent.com/Q-Future/Q-Align/main/fig/singapore_flyer.jpg"
+print("The quality score of this image is {}".format(model.score(image_path_url)) 
+```
+
+## Evaluation with GitHub
+```shell
+git clone https://github.com/Q-Future/Compare2Score.git
+cd Compare2Score
+pip install -e .
+```
+
+```python
+from q_align import Compare2Scorer
+from PIL import Image
+
+scorer = Compare2Scorer()
+image_path = "figs/i04_03_4.bmp"
+print("The quality score of this image is {}.".format(scorer(image_path)))
+```
+
+## Citation
+
+```bibtex
+@article{zhu2024adaptive,
+  title={Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare},
+  author={Zhu, Hanwei and Wu, Haoning and Li, Yixuan and Zhang, Zicheng and Chen, Baoliang and Zhu, Lingyu and Fang, Yuming and Zhai, Guangtao and Lin, Weisi and Wang, Shiqi},
+  journal={arXiv preprint arXiv:2405.19298},
+  year={2024},
+}
+```