diff --git a/scripts/cal_flops.py b/scripts/cal_flops.py
index 32526d89..d60a048b 100644
--- a/scripts/cal_flops.py
+++ b/scripts/cal_flops.py
@@ -36,9 +36,11 @@ def calculate_flops(
     """
     with get_accelerator().device(0):
         chat_model = ChatModel(dict(model_name_or_path=model_name_or_path, template="empty", flash_attn=flash_attn))
-        fake_input = torch.ones((batch_size, seq_length), dtype=torch.long, device=chat_model.model.device)
+        fake_input = torch.ones((batch_size, seq_length), dtype=torch.long, device=chat_model.engine.model.device)
         input_dict = {"input_ids": fake_input, "labels": fake_input.clone()}
-        flops, macs, params = get_model_profile(chat_model.model, kwargs=input_dict, print_profile=True, detailed=True)
+        flops, macs, params = get_model_profile(
+            chat_model.engine.model, kwargs=input_dict, print_profile=True, detailed=True
+        )
         print("FLOPs:", flops)
         print("MACs:", macs)
         print("Params:", params)
diff --git a/src/llamafactory/webui/runner.py b/src/llamafactory/webui/runner.py
index 9bc69679..4ce35a02 100644
--- a/src/llamafactory/webui/runner.py
+++ b/src/llamafactory/webui/runner.py
@@ -104,11 +104,6 @@ class Runner:
         model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
         user_config = load_config()
 
-        if get("top.quantization_bit") in QUANTIZATION_BITS:
-            quantization_bit = int(get("top.quantization_bit"))
-        else:
-            quantization_bit = None
-
         args = dict(
             stage=TRAINING_STAGES[get("train.training_stage")],
             do_train=True,
@@ -116,8 +111,6 @@ class Runner:
             cache_dir=user_config.get("cache_dir", None),
             preprocessing_num_workers=16,
             finetuning_type=finetuning_type,
-            quantization_bit=quantization_bit,
-            quantization_method=get("top.quantization_method"),
             template=get("top.template"),
             rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
             flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
@@ -166,6 +159,11 @@ class Runner:
             else:  # str
                 args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, get("top.checkpoint_path"))
 
+        # quantization
+        if get("top.quantization_bit") in QUANTIZATION_BITS:
+            args["quantization_bit"] = int(get("top.quantization_bit"))
+            args["quantization_method"] = get("top.quantization_method")
+
         # freeze config
         if args["finetuning_type"] == "freeze":
             args["freeze_trainable_layers"] = get("train.freeze_trainable_layers")
@@ -242,18 +240,12 @@ class Runner:
         model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
         user_config = load_config()
 
-        if get("top.quantization_bit") in QUANTIZATION_BITS:
-            quantization_bit = int(get("top.quantization_bit"))
-        else:
-            quantization_bit = None
-
         args = dict(
             stage="sft",
             model_name_or_path=get("top.model_path"),
             cache_dir=user_config.get("cache_dir", None),
             preprocessing_num_workers=16,
             finetuning_type=finetuning_type,
-            quantization_bit=quantization_bit,
             quantization_method=get("top.quantization_method"),
             template=get("top.template"),
             rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
@@ -277,6 +269,7 @@ class Runner:
         else:
             args["do_eval"] = True
 
+        # checkpoints
         if get("top.checkpoint_path"):
             if finetuning_type in PEFT_METHODS:  # list
                 args["adapter_name_or_path"] = ",".join(
@@ -285,6 +278,11 @@ class Runner:
             else:  # str
                 args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, get("top.checkpoint_path"))
 
+        # quantization
+        if get("top.quantization_bit") in QUANTIZATION_BITS:
+            args["quantization_bit"] = int(get("top.quantization_bit"))
+            args["quantization_method"] = get("top.quantization_method")
+
         return args
 
     def _preview(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", str], None, None]: