diff --git a/dbgpt_hub/configs/data_args.py b/dbgpt_hub/configs/data_args.py
index c861723..9281e44 100644
--- a/dbgpt_hub/configs/data_args.py
+++ b/dbgpt_hub/configs/data_args.py
@@ -156,6 +156,13 @@ class DataArguments:
             "help": "Size of the development set, should be an integer or a float in range `[0,1)`."
         },
     )
+    predicted_out_filename: Optional[str] = field(
+        default="pred_sql.sql",
+        metadata={
+            "help": "Filename to save predicted outcomes"
+        },
+    )
+
 
     def init_for_training(self):  # support mixing multiple datasets
         dataset_names = [ds.strip() for ds in self.dataset.split(",")]
diff --git a/dbgpt_hub/llm_base/chat_model.py b/dbgpt_hub/llm_base/chat_model.py
index b9fc15c..1d14384 100644
--- a/dbgpt_hub/llm_base/chat_model.py
+++ b/dbgpt_hub/llm_base/chat_model.py
@@ -16,12 +16,12 @@
 class ChatModel:
 
     def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
-        model_args, data_args, finetuning_args, self.generating_args = get_infer_args(args)
+        model_args, self.data_args, finetuning_args, self.generating_args = get_infer_args(args)
         self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
         self.tokenizer.padding_side = "left"
         self.model = dispatch_model(self.model)
-        self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer)
-        self.system_prompt = data_args.system_prompt
+        self.template = get_template_and_fix_tokenizer(self.data_args.template, self.tokenizer)
+        self.system_prompt = self.data_args.system_prompt
 
     def process_args(
         self,
diff --git a/dbgpt_hub/predict/predict.py b/dbgpt_hub/predict/predict.py
index d7da933..4c89a30 100644
--- a/dbgpt_hub/predict/predict.py
+++ b/dbgpt_hub/predict/predict.py
@@ -18,8 +18,8 @@ def prepare_dataset() -> List[Dict]:
 def inference(model: ChatModel, predict_data: List[Dict], **input_kwargs):
     res = []
     #test
-    for item in predict_data[:20]:
-    # for item in predict_data:
+    # for item in predict_data[:20]:
+    for item in predict_data:
         response, _ = model.chat(
             query=item["input"], 
             history=[],
@@ -36,7 +36,7 @@ def main():
     if not os.path.exists(predict_out_dir):
         os.mkdir(predict_out_dir)
     
-    predict_output_dir_name = os.path.join(predict_out_dir, PREDICTED_OUT_FILENAME)
+    predict_output_dir_name = os.path.join(predict_out_dir, model.data_args.predicted_out_filename)
     print(f"predict_output_dir_name \t{predict_output_dir_name}")
     
     with open(predict_output_dir_name, "w") as f:
diff --git a/dbgpt_hub/scripts/predict_sft.sh b/dbgpt_hub/scripts/predict_sft.sh
index 953e747..5f2c4c9 100644
--- a/dbgpt_hub/scripts/predict_sft.sh
+++ b/dbgpt_hub/scripts/predict_sft.sh
@@ -1,17 +1,18 @@
 ## shijian llama2 test
-# CUDA_VISIBLE_DEVICES=0,1  python dbgpt_hub/predict/predict.py \
-#     --model_name_or_path Llama-2-13b-chat-hf \
-#     --template llama2 \
-#     --finetuning_type lora \
-#     --checkpoint_dir dbgpt_hub/output/adapter
+CUDA_VISIBLE_DEVICES=0,1  python dbgpt_hub/predict/predict.py \
+    --model_name_or_path Llama-2-13b-chat-hf \
+    --template llama2 \
+    --finetuning_type lora \
+    --checkpoint_dir dbgpt_hub/output/adapter/llama2-13b-qlora \
+    --predicted_out_filename pred_sql.sql
 
 # # wangzai baichua2_eval test
-CUDA_VISIBLE_DEVICES=0 python dbgpt_hub/predict/predict.py \
-    --model_name_or_path /home/model/Baichuan2-13B-Chat \
-    --template baichuan2_eval \
-    --quantization_bit 4 \
-    --finetuning_type lora \
-    --checkpoint_dir dbgpt_hub/output/adapter/baichuan2-13b-qlora 
+# CUDA_VISIBLE_DEVICES=0 python dbgpt_hub/predict/predict.py \
+#     --model_name_or_path /home/model/Baichuan2-13B-Chat \
+#     --template baichuan2_eval \
+#     --quantization_bit 4 \
+#     --finetuning_type lora \
+#     --checkpoint_dir dbgpt_hub/output/adapter/baichuan2-13b-qlora 
 
 
 ## wangzai codellama2_pred test a100