diff --git a/scripts/loftq_init.py b/scripts/loftq_init.py
index b9506fa3..4d2c01b9 100644
--- a/scripts/loftq_init.py
+++ b/scripts/loftq_init.py
@@ -36,15 +36,19 @@ def quantize_loftq(
     lora_alpha: int = None,
     lora_rank: int = 16,
     lora_dropout: float = 0,
-    lora_target: str = "q_proj,v_proj",
+    lora_target: tuple = ("q_proj", "v_proj"),
     save_safetensors: bool = True,
 ):
     r"""
     Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
     Usage: python loftq_init.py --model_name_or_path path_to_model --output_dir output_dir
     """
+    if isinstance(lora_target, str):
+        lora_target = [name.strip() for name in lora_target.split(",")]
+
     tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
+
     loftq_config = LoftQConfig(loftq_bits=loftq_bits, loftq_iter=loftq_iter)
     lora_config = LoraConfig(
         task_type=TaskType.CAUSAL_LM,
@@ -52,7 +56,7 @@ def quantize_loftq(
         r=lora_rank,
         lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
         lora_dropout=lora_dropout,
-        target_modules=[name.strip() for name in lora_target.split(",")],
+        target_modules=lora_target,
         init_lora_weights="loftq",
         loftq_config=loftq_config,
     )
diff --git a/scripts/pissa_init.py b/scripts/pissa_init.py
index 50239727..ad9d161c 100644
--- a/scripts/pissa_init.py
+++ b/scripts/pissa_init.py
@@ -35,21 +35,25 @@ def quantize_pissa(
     lora_alpha: int = None,
     lora_rank: int = 16,
     lora_dropout: float = 0,
-    lora_target: str = "q_proj,v_proj",
+    lora_target: tuple = ("q_proj", "v_proj"),
     save_safetensors: bool = True,
 ):
     r"""
     Initializes LoRA weights with Principal Singular values and Singular vectors Adaptation (PiSSA)
     Usage: python pissa_init.py --model_name_or_path path_to_model --output_dir output_dir
     """
+    if isinstance(lora_target, str):
+        lora_target = [name.strip() for name in lora_target.split(",")]
+
     tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
+
     lora_config = LoraConfig(
         task_type=TaskType.CAUSAL_LM,
         r=lora_rank,
         lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
         lora_dropout=lora_dropout,
-        target_modules=[name.strip() for name in lora_target.split(",")],
+        target_modules=lora_target,
         init_lora_weights="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter),
     )
 
diff --git a/src/llamafactory/data/template.py b/src/llamafactory/data/template.py
index 193ff482..53f16df4 100644
--- a/src/llamafactory/data/template.py
+++ b/src/llamafactory/data/template.py
@@ -618,7 +618,7 @@ _register_template(
 
 _register_template(
     name="default",
-    format_user=StringFormatter(slots=["Human: {{content}}\nAssistant: "]),
+    format_user=StringFormatter(slots=["Human: {{content}}\nAssistant:"]),
     format_system=StringFormatter(slots=["{{content}}\n"]),
     format_separator=EmptyFormatter(slots=["\n"]),
 )