diff --git a/src/llmtuner/hparams/finetuning_args.py b/src/llmtuner/hparams/finetuning_args.py
index eb949626..3efa073e 100644
--- a/src/llmtuner/hparams/finetuning_args.py
+++ b/src/llmtuner/hparams/finetuning_args.py
@@ -20,22 +20,23 @@ class FinetuningArguments:
         default=3,
         metadata={"help": "Number of trainable layers for partial-parameter (freeze) fine-tuning."}
     )
-    name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
+    name_module_trainable: Optional[str] = field(
         default="mlp",
         metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \
+                  Use commas to separate multiple modules. \
                   LLaMA choices: [\"mlp\", \"self_attn\"], \
                   BLOOM & Falcon & ChatGLM choices: [\"mlp\", \"self_attention\"], \
                   Qwen choices: [\"mlp\", \"attn\"], \
                   Phi-1.5 choices: [\"mlp\", \"mixer\"], \
-                  LLaMA-2, BlueLM, Baichuan, InternLM, Mistral, Skywork, XVERSE, Yi choices: the same as LLaMA."}
+                  Others choices: the same as LLaMA."}
     )
     lora_rank: Optional[int] = field(
         default=8,
         metadata={"help": "The intrinsic dimension for LoRA fine-tuning."}
     )
     lora_alpha: Optional[float] = field(
-        default=32.0,
-        metadata={"help": "The scale factor for LoRA fine-tuning (similar with the learning rate)."}
+        default=None,
+        metadata={"help": "The scale factor for LoRA fine-tuning (default: lora_rank * 2.0)."}
     )
     lora_dropout: Optional[float] = field(
         default=0.1,
@@ -49,7 +50,7 @@ class FinetuningArguments:
                   Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
                   Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
                   Phi-1.5 choices: [\"Wqkv\", \"out_proj\", \"fc1\", \"fc2\"], \
-                  LLaMA-2, BlueLM, InternLM, Mistral, Skywork, XVERSE, Yi choices: the same as LLaMA."}
+                  Others choices: the same as LLaMA."}
     )
     additional_target: Optional[str] = field(
         default=None,
@@ -93,12 +94,15 @@ class FinetuningArguments:
     )
 
     def __post_init__(self):
-        if isinstance(self.lora_target, str): # support custom target modules/layers of LoRA
-            self.lora_target = [target.strip() for target in self.lora_target.split(",")]
-
-        if isinstance(self.additional_target, str):
-            self.additional_target = [target.strip() for target in self.additional_target.split(",")]
+        def split_arg(arg):
+            if isinstance(arg, str):
+                return [item.strip() for item in arg.split(",")]
+            return arg
 
+        self.name_module_trainable = split_arg(self.name_module_trainable)
+        self.lora_alpha = self.lora_alpha or float(self.lora_rank * 2.0)
+        self.lora_target = split_arg(self.lora_target)
+        self.additional_target = split_arg(self.additional_target)
         assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
 
     def save_to_json(self, json_path: str):
diff --git a/src/llmtuner/model/adapter.py b/src/llmtuner/model/adapter.py
index 6873ede1..110687de 100644
--- a/src/llmtuner/model/adapter.py
+++ b/src/llmtuner/model/adapter.py
@@ -46,7 +46,11 @@ def init_adapter(
         else: # fine-tuning the first n layers if num_layer_trainable < 0
             trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)]
 
-        trainable_layers = ["{:d}.{}".format(idx, finetuning_args.name_module_trainable) for idx in trainable_layer_ids]
+        trainable_layers = []
+        for module_name in finetuning_args.name_module_trainable:
+            for idx in trainable_layer_ids:
+                trainable_layers.append("{:d}.{}".format(idx, module_name))
+
         for name, param in model.named_parameters():
             if not any(trainable_layer in name for trainable_layer in trainable_layers):
                 param.requires_grad_(False)