diff --git a/src/llmtuner/hparams/finetuning_args.py b/src/llmtuner/hparams/finetuning_args.py
index 23bb324b..f43e5786 100644
--- a/src/llmtuner/hparams/finetuning_args.py
+++ b/src/llmtuner/hparams/finetuning_args.py
@@ -16,9 +16,10 @@ class FinetuningArguments:
         default=32,
         metadata={"help": "Number of decoder blocks in the model. \
                   LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
+                  LLaMA-2 choices: [\"32\", \"40\", \"80\"], \
                   BLOOM choices: [\"24\", \"30\", \"70\"], \
                   Falcon choices: [\"32\", \"60\"], \
-                  Baichuan choices: [\"32\"]"}
+                  Baichuan choices: [\"32\", \"40\"]"}
     )
     num_layer_trainable: Optional[int] = field(
         default=3,
@@ -27,7 +28,7 @@ class FinetuningArguments:
     name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
         default="mlp",
         metadata={"help": "Name of trainable modules for Freeze fine-tuning. \
-                  LLaMA choices: [\"mlp\", \"self_attn\"], \
+                  LLaMA & LLaMA-2 choices: [\"mlp\", \"self_attn\"], \
                   BLOOM & Falcon choices: [\"mlp\", \"self_attention\"], \
                   Baichuan choices: [\"mlp\", \"self_attn\"]"}
     )
@@ -46,7 +47,7 @@ class FinetuningArguments:
     lora_target: Optional[str] = field(
         default="q_proj,v_proj",
         metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
-                  LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
+                  LLaMA & LLaMA-2 choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
                   BLOOM & Falcon choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \
                   Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"]"}
     )