diff --git a/dbgpt_hub/llm_base/config_parser.py b/dbgpt_hub/llm_base/config_parser.py
index be12508..0129b69 100644
--- a/dbgpt_hub/llm_base/config_parser.py
+++ b/dbgpt_hub/llm_base/config_parser.py
@@ -93,7 +93,8 @@ def parse_infer_args(
 
 
 def get_train_args(
-    args: Optional[Dict[str, Any]] = None
+    args: Optional[Dict[str, Any]] = None,
+    data_args_init: bool = True
 ) -> Tuple[
     ModelArguments,
     DataArguments,
@@ -121,7 +122,8 @@ def get_train_args(
     transformers.utils.logging.enable_explicit_format()
 
     # Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
-    data_args.init_for_training()
+    if data_args_init:
+        data_args.init_for_training()
 
     if training_args.max_steps == -1 and data_args.streaming:
         raise ValueError("Please specify `max_steps` in streaming mode.")
diff --git a/dbgpt_hub/llm_base/model_trainer.py b/dbgpt_hub/llm_base/model_trainer.py
index fd51f69..228be35 100644
--- a/dbgpt_hub/llm_base/model_trainer.py
+++ b/dbgpt_hub/llm_base/model_trainer.py
@@ -401,7 +401,7 @@ def plot_loss(
 def export_model(
     args: Optional[Dict[str, Any]] = None, max_shard_size: Optional[str] = "10GB"
 ):
-    model_args, _, training_args, finetuning_args, _ = get_train_args(args)
+    model_args, _, training_args, finetuning_args, _ = get_train_args(args, data_args_init=False)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
     model.save_pretrained(training_args.output_dir, max_shard_size=max_shard_size)
     try: