[Chore][Doc] uses model id determined from OpenAI client

examples/online_serving/openai_chat_completion_structured_outputs.py

@@ -138,7 +138,7 @@ def main():
         api_key="-",
     )
 
-    model = "Qwen/Qwen2.5-3B-Instruct"
+    model = client.models.list().data[0].id
 
     print("Guided Choice Completion:")
     print(guided_choice_completion(client, model))

examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py

@@ -59,7 +59,7 @@ def main():
     }]
 
     response = client.chat.completions.create(
-        model="meta-llama/Llama-3.1-8B-Instruct",
+        model=client.models.list().data[0].id,
         messages=messages,
         response_format={
             "type":

examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py

@@ -4,7 +4,7 @@
 like DeepSeekR1. The thinking process will not be guided by the JSON
 schema provided by the user. Only the final output will be structured.
 
-To run this example, you need to start the vLLM server with the reasoning 
+To run this example, you need to start the vLLM server with the reasoning
 parser:
 
 ```bash