diff --git a/examples/online_serving/openai_chat_completion_structured_outputs.py b/examples/online_serving/openai_chat_completion_structured_outputs.py index 9c57af1c158c1..660369e55d40e 100644 --- a/examples/online_serving/openai_chat_completion_structured_outputs.py +++ b/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)) diff --git a/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py b/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py index b807bc5405262..42aa12c451c04 100644 --- a/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py +++ b/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py @@ -59,7 +59,7 @@ and San Francisco? }] 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": diff --git a/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py b/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py index 5da9236c53067..a04f0cdf12f76 100644 --- a/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py +++ b/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py @@ -4,7 +4,7 @@ An example shows how to generate structured outputs from reasoning models 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