Add pipeline tag and library name, improve description

README.md

@@ -1,11 +1,21 @@
 ---
 license: mit
+pipeline_tag: image-to-text
+library_name: transformers
 ---
-<h2>[Installation Free!] Quicker Start with Hugging Face AutoModel</h2> 
 
-No need to install this GitHub repo. Ensure that you use the Transformers package of 4.45.0 (`pip install transformers==4.45.0`).
+# Q-SiT:  Image Quality Scoring and Interpreting with Large Language Models
+
+Q-SiT is a model for image quality scoring and interpretation. It uses a Large Language Model to perform both tasks simultaneously, recognizing the inherent connection between perception and decision-making in the human visual system.  Unlike previous approaches which treat scoring and interpreting as separate tasks, Q-SiT provides a unified framework.
+
+Project page: https://github.com/Q-Future/Q-SiT
+
+## Quicker Start with Hugging Face AutoModel
+
+No need to install this GitHub repo. Ensure that you use the Transformers package version 4.45.0 (`pip install transformers==4.45.0`).
+
+### Image Quality Interpreting Chat
 
-Do the image quality interpreting chat with q-sit.
 ```python
 import requests
 from PIL import Image
@@ -45,7 +55,8 @@ print(processor.decode(output[0][2:], skip_special_tokens=True).split("assistant
 # very low
 ```
 
-Do the image quality scoring with q-sit.
+### Image Quality Scoring
+
 ```python
 import torch
 import requests
@@ -77,7 +88,9 @@ conversation = [
     {
         "role": "user",
         "content": [
-            {"type": "text", "text": "Assume you are an image quality evaluator. \nYour rating should be chosen from the following five categories: Excellent, Good, Fair, Poor, and Bad (from high to low). \nHow would you rate the quality of this image?"},
+            {"type": "text", "text": "Assume you are an image quality evaluator. 
+Your rating should be chosen from the following five categories: Excellent, Good, Fair, Poor, and Bad (from high to low). 
+How would you rate the quality of this image?"},
             {"type": "image"},
         ],
     },
@@ -111,4 +124,8 @@ print("Weighted average score:", weighted_score)
 # if you want range from 0-5, multiply 5
 ```
 
-To test q-sit on datasets, please refer to evaluation scripts [here](https://github.com/Q-Future/Q-SiT/tree/main/eval_scripts).
+For dataset evaluation scripts, please refer to [this directory](https://github.com/Q-Future/Q-SiT/tree/main/eval_scripts).  For training information, see the [Training Q-SiT](https://github.com/Q-Future/Q-SiT#training-q-sit) section of the GitHub repository.
+
+## Citation
+
+To do