Add basic vllm usage

README.md

@@ -101,6 +101,109 @@ scores = model.process(inputs)
 print(scores)
 # [0.8613124489784241, 0.6757137179374695, 0.8125371336936951]
 ```
+
+### vLLM Basic Usage Example
+```python
+import argparse
+import os
+from pathlib import Path
+from typing import Dict, Any
+from vllm import LLM, EngineArgs
+from vllm.entrypoints.score_utils import ScoreMultiModalParam
+
+
+queries = [
+    {"text": "A woman playing with her dog on a beach at sunset."}
+]
+
+documents = [
+    {"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust."},
+    {"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
+    {"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust.", 
+     "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}
+]
+
+
+def format_document_to_score_param(doc_dict: Dict[str, Any]) -> ScoreMultiModalParam:
+    content = []
+    
+    text = doc_dict.get('text')
+    image = doc_dict.get('image')
+    
+    if text:
+        content.append({
+            "type": "text",
+            "text": text
+        })
+    
+    if image:
+        image_url = image
+        if isinstance(image, str) and not image.startswith(('http', 'https', 'oss')):
+            abs_image_path = os.path.abspath(image)
+            image_url = 'file://' + abs_image_path
+        
+        content.append({
+            "type": "image_url",
+            "image_url": {
+                "url": image_url
+            }
+        })
+    
+    if not content:
+        content.append({
+            "type": "text",
+            "text": ""
+        })
+    
+    return {"content": content}
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Offline Reranker with vLLM")
+    parser.add_argument("--model-path", type=str, default="models/Qwen3-VL-Reranker-2B", help="Path to the reranker model")
+    parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type (e.g., bfloat16)")
+    parser.add_argument("--template-path", type=str, default="vllm/examples/pooling/score/template/qwen3_vl_reranker.jinja", 
+                        help="Path to chat template file")
+    args = parser.parse_args()
+    
+    print(f"Loading model from {args.model_path}...")
+    
+    engine_args = EngineArgs(
+        model=args.model_path,
+        runner="pooling",
+        dtype=args.dtype,
+        trust_remote_code=True,
+        hf_overrides={
+            "architectures": ["Qwen3VLForSequenceClassification"],
+            "classifier_from_token": ["no", "yes"],
+            "is_original_qwen3_reranker": True,
+        },
+    )
+    
+    llm = LLM(**vars(engine_args))
+    
+    template_path = Path(args.template_path)
+    chat_template = template_path.read_text() if template_path.exists() else None
+    
+    for query_dict in queries:
+        query_text = query_dict.get('text', '')
+        print(f"\nQuery: {query_text}")
+        
+        scores = []
+        for doc_dict in documents:
+            doc_param = format_document_to_score_param(doc_dict)
+            outputs = llm.score(query_text, doc_param, chat_template=chat_template)
+            score = outputs[0].outputs.score
+            scores.append(score)
+        
+        print(scores)
+
+
+if __name__ == "__main__":
+    main()
+
+```
+
 For more usage examples, please visit our [GitHub repository](https://github.com/QwenLM/Qwen3-VL-Embedding).
 
 ## Citation