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README.md

@@ -1,14 +1,61 @@
 ---
-license: mit
 datasets:
 - PAPOGalaxy/PAPO_train
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
-
 # PAPO Model
 
-## Model Source
-This is the official model released for paper **PAPO: Perception-Aware Policy Optimization for Multimodal Reasoning** (arxiv.org/abs/2507.06448)
+This is the official model released for the paper [**Perception-Aware Policy Optimization for Multimodal Reasoning**](https://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=0.02)
+PAPO (γ=0.02)
+
+## Usage
+
+This model can be loaded and used with the `transformers` library.
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+import requests
+
+# Load the processor and model
+# Note: Replace "PAPOGalaxy/PAPO-Qwen2.5-7B" with the actual model ID if different
+processor = AutoProcessor.from_pretrained("PAPOGalaxy/PAPO-Qwen2.5-7B", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("PAPOGalaxy/PAPO-Qwen2.5-7B", trust_remote_code=True)
+
+# Example image (replace with your image URL or local path)
+image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/preprocessor_config_vln.png"
+image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
+
+# Define your prompt
+prompt = "What are the main objects in this image?"
+
+# Format messages for the model
+messages = [
+    {"role": "user", "content": [{"type": "image", "content": image}, {"type": "text", "text": prompt}]}
+]
+
+# Apply chat template and tokenize
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+input_ids = processor(text, return_tensors="pt").input_ids
+
+# Generate response
+output_ids = model.generate(
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+# Decode and print the generated text
+generated_text = processor.decode(output_ids[0], skip_special_tokens=True)
+print(generated_text)
+```