Improve model card: Add pipeline tag, library, links, and usage example (#1)

- Improve model card: Add pipeline tag, library, links, and usage example (774ee033792b795656cbc0015f5cfea70ef20099)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,14 +1,56 @@
 ---
-license: mit
 datasets:
 - PAPOGalaxy/PAPO_train
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
+# PAPO: Perception-Aware Policy Optimization for Multimodal Reasoning
 
-# PAPO Model
+This is the official model released for our paper [Perception-Aware Policy Optimization for Multimodal Reasoning](https://huggingface.co/papers/2507.06448).
 
-## Model Source
-This is the official model released for our paper **PAPO: Perception-Aware Policy Optimization for Multimodal Reasoning** (arxiv.org/abs/2507.06448)
+**Project Page:** [https://mikewangwzhl.github.io/PAPO/](https://mikewangwzhl.github.io/PAPO/)
+**Code:** [https://github.com/mikewangwzhl/PAPO](https://github.com/mikewangwzhl/PAPO)
 
 ## Model Version
-PAPO (γ=0.01)
+PAPO (γ=0.01)
+
+## Usage
+
+You can use this model with the Hugging Face `transformers` library.
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+import requests
+
+# Replace "PAPOGalaxy/PAPO" with the actual model ID if different
+# For example, if it's PAPOGalaxy/PAPO-7B or PAPOGalaxy/PAPO-3B
+model_id = "PAPOGalaxy/PAPO" 
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+# Example image (replace with your own image path or URL)
+image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/bee.JPG"
+image = Image.open(requests.get(image_url, stream=True).raw)
+
+# Example prompt
+prompt = "What is in the image?"
+
+# Prepare inputs following the model's chat template
+messages = [
+    {"role": "user", "content": [
+        {"type": "image", "image": image}, 
+        {"type": "text", "text": prompt}
+    ]}
+]
+text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+inputs = processor(text=text, images=image, return_tensors="pt").to(model.device)
+
+# Generate response
+generated_ids = model.generate(**inputs, max_new_tokens=100)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(generated_text)
+```