Upload generate_responses.py with huggingface_hub

generate_responses.py

@@ -67,6 +67,10 @@ def extract_output_payload(output) -> dict[str, Optional[str]]:
     """Convert a vLLM chat result into a dataset-serializable dict."""
     completion = output.outputs[0] if getattr(output, "outputs", None) else None
     text = getattr(completion, "text", "") if completion is not None else ""
+    print("COMPLETION", completion)
+    print("hasattr reasoning_content", hasattr(completion, "reasoning_content"))
+    print("hasattr reasoning", hasattr(completion, "reasoning"))
+    print("completion attrs", dir(completion))
 
     reasoning_content = None
     if completion is not None:
@@ -189,6 +193,7 @@ def main(
     src_dataset_hub_id: str,
     output_dataset_hub_id: str,
     model_id: str = "Qwen/Qwen3-30B-A3B-Instruct-2507",
+    reasoning_parser: Optional[str] = None,
     messages_column: str = "messages",
     prompt_column: Optional[str] = None,
     output_column: str = "outputs",
@@ -213,6 +218,7 @@ def main(
         src_dataset_hub_id: Input dataset on Hugging Face Hub
         output_dataset_hub_id: Where to save results on Hugging Face Hub
         model_id: Hugging Face model ID for generation
+        reasoning_parser: Optional vLLM reasoning parser to enable structured reasoning extraction
         messages_column: Column name containing chat messages
         prompt_column: Column name containing plain text prompts (alternative to messages_column)
         output_column: Column name for generated responses
@@ -265,6 +271,9 @@ def main(
     if max_model_len is not None:
         vllm_kwargs["max_model_len"] = max_model_len
         logger.info(f"Using max_model_len={max_model_len}")
+    if reasoning_parser is not None:
+        vllm_kwargs["reasoning_parser"] = reasoning_parser
+        logger.info(f"Using reasoning_parser={reasoning_parser}")
 
     llm = LLM(**vllm_kwargs)
 
@@ -418,6 +427,11 @@ Examples:
             default="messages",
             help="Column containing chat messages (default: messages)",
         )
+        parser.add_argument(
+            "--reasoning-parser",
+            type=str,
+            help="vLLM reasoning parser to use for supported models",
+        )
         parser.add_argument(
             "--prompt-column",
             type=str,
@@ -516,6 +530,7 @@ Examples:
             src_dataset_hub_id=args.src_dataset_hub_id,
             output_dataset_hub_id=args.output_dataset_hub_id,
             model_id=args.model_id,
+            reasoning_parser=args.reasoning_parser,
             messages_column=args.messages_column,
             prompt_column=args.prompt_column,
             output_column=args.output_column,