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[Chore][Doc] uses model id determined from OpenAI client (#17815)

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Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
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Aaron Pham 2025-05-07 21:48:57 -04:00 committed by GitHub
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3 changed files with 3 additions and 3 deletions
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2
examples/online_serving/openai_chat_completion_structured_outputs.py
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@ -138,7 +138,7 @@ def main():
api_key="-", api_key="-",
) )
model = "Qwen/Qwen2.5-3B-Instruct" model = client.models.list().data[0].id
print("Guided Choice Completion:") print("Guided Choice Completion:")
print(guided_choice_completion(client, model)) print(guided_choice_completion(client, model))

2
examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py
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@ -59,7 +59,7 @@ and San Francisco?
}] }]
response = client.chat.completions.create( response = client.chat.completions.create(
model="meta-llama/Llama-3.1-8B-Instruct", model=client.models.list().data[0].id,
messages=messages, messages=messages,
response_format={ response_format={
"type": "type":

2
examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
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@ -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 like DeepSeekR1. The thinking process will not be guided by the JSON
schema provided by the user. Only the final output will be structured. 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: parser:
```bash ```bash