add

20251014.md

@@ -74,7 +74,7 @@ from pydantic import BaseModel
 from vllm import LLM, SamplingParams
 from vllm.sampling_params import GuidedDecodingParams
 
-# Guided decoding by JSON using Pydantic schema
+# 定义结构化输出 schema
 class CarType(str, Enum):
     sedan = "sedan"
     suv = "SUV"
@@ -86,25 +86,52 @@ class CarDescription(BaseModel):
     model: str
     car_type: CarType
 
+# 获取 JSON schema
 json_schema = CarDescription.model_json_schema()
-# guided_decoding_params_json = GuidedDecodingParams(json=json_schema)
+
-sampling_params_json = SamplingParams(guided_decoding={})
+# 设置 prompt
-prompt_json = (
+prompt = (
-    "Generate a JSON with the brand, model and car_type of"
+    "Generate a JSON with the brand, model and car_type of "
     "the most iconic car from the 90's"
 )
 
 def format_output(title: str, output: str):
     print(f"{'-' * 50}\n{title}: {output}\n{'-' * 50}")
 
-def generate_output(prompt: str, sampling_params: SamplingParams, llm: LLM):
-    outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
-    return outputs[0].outputs[0].text
-
 def main():
-    llm = LLM(model="qwen", max_model_len=100)
+    # 1. 初始化本地 LLM，加载本地模型文件
-    json_output = generate_output(prompt_json, sampling_params_json, llm)
+    llm = LLM(
-    format_output("Guided decoding by JSON", json_output)
+        model="/home/ss/vllm-py12/qwen3-06b",  # 指向你的本地模型路径
+        max_model_len=1024,
+        enable_prefix_caching=True,
+        gpu_memory_utilization=0.9,
+    )
+
+    # 2. 构造一个无效的 guided_decoding：没有任何有效字段
+    # 这将导致 get_structured_output_key() 中 raise ValueError
+    guided_decoding_invalid = GuidedDecodingParams(
+        json=None,
+        json_object=False,
+        regex=None,
+        choice=None,
+        grammar=None,
+        structural_tag=None
+    )
+
+    sampling_params = SamplingParams(
+        temperature=0.0,
+        max_tokens=512,
+        guided_decoding=guided_decoding_invalid  # ✅ 传入但无有效字段
+    )
+
+    # 3. 生成输出（预期会触发 ValueError）
+    try:
+        outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
+        for output in outputs:
+            generated_text = output.outputs[0].text
+            format_output("Output", generated_text)
+    except Exception as e:
+        print(f"Caught expected error: {e}")
 
 if __name__ == "__main__":
     main()