[data] qwen3 fixes (#8109)

scripts/vllm_infer.py

@@ -49,6 +49,8 @@ def vllm_infer(
     max_new_tokens: int = 1024,
     repetition_penalty: float = 1.0,
     skip_special_tokens: bool = True,
+    default_system: Optional[str] = None,
+    enable_thinking: bool = True,
     seed: Optional[int] = None,
     pipeline_parallel_size: int = 1,
     image_max_pixels: int = 768 * 768,
@@ -74,6 +76,8 @@ def vllm_infer(
             cutoff_len=cutoff_len,
             max_samples=max_samples,
             preprocessing_num_workers=16,
+            default_system=default_system,
+            enable_thinking=enable_thinking,
             vllm_config=vllm_config,
             temperature=temperature,
             top_p=top_p,
@@ -127,14 +131,11 @@ def vllm_infer(
         lora_request = None
 
     # Store all results in these lists
-    all_prompts = []
-    all_preds = []
-    all_labels = []
+    all_prompts, all_preds, all_labels = [], [], []
 
     # Add batch process to avoid the issue of too many files opened
     for i in tqdm(range(0, len(train_dataset), batch_size), desc="Processing batched inference"):
         vllm_inputs, prompts, labels = [], [], []
-
         batch = train_dataset[i : min(i + batch_size, len(train_dataset))]
 
         for j in range(len(batch["input_ids"])):
@@ -176,15 +177,14 @@ def vllm_infer(
             )
 
         results = llm.generate(vllm_inputs, sampling_params, lora_request=lora_request)
-
         preds = [result.outputs[0].text for result in results]
 
         # Accumulate results
         all_prompts.extend(prompts)
         all_preds.extend(preds)
         all_labels.extend(labels)
-
         gc.collect()
+
     # Write all results at once outside the loop
     with open(save_name, "w", encoding="utf-8") as f:
         for text, pred, label in zip(all_prompts, all_preds, all_labels):