update README

README.md

@@ -1,5 +1,78 @@
 ---
-library_name: transformers
-license: cc-by-4.0
+license: cc-by-nc-4.0
 ---
 
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+
+<div align="center">
+<h1>
+  RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability
+</h1>
+</div>
+
+<p align="center">
+📝 <a href="" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/Deepnoid/RadZero" target="_blank">Hugging Face</a> • 🧩 <a href="" target="_blank">Github</a>
+</p>
+
+<div align="center">
+</div>
+
+
+## 🎬 Get Started
+
+```python
+# Deepnoid/RadZero/inference.py
+import warnings
+
+import torch
+from transformers import AutoImageProcessor, AutoModel, AutoTokenizer
+
+from utils import model_inference
+
+# Suppress specific warnings for cleaner logs
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+def load_model(device, dtype):
+
+    tokenizer = AutoTokenizer.from_pretrained("Deepnoid/RadZero")
+    image_processor = AutoImageProcessor.from_pretrained("Deepnoid/RadZero")
+
+    model = AutoModel.from_pretrained(
+        "Deepnoid/RadZero",
+        trust_remote_code=True,
+        torch_dtype=dtype,
+        device_map=device,
+    )
+
+    models = {
+        "tokenizer": tokenizer,
+        "image_processor": image_processor,
+        "model": model,
+    }
+    return models
+
+
+if __name__ == "__main__":
+    # Setup constant
+    device = torch.device("cuda")
+    dtype = torch.float32
+
+    # load models
+    models = load_model(device, dtype)
+
+    # load image
+    image_path = "cxr_image.jpg"
+
+    # inference
+    similarity_prob, similarity_map = model_inference(
+        image_path, "There is fibrosis", **models
+    )
+
+    print(similarity_prob)
+    print(similarity_map.min())
+    print(similarity_map.max())
+    print(similarity_map.shape)
+```
+