diff --git a/merge.py b/merge-HF-and-lora-to-HF.py
similarity index 50%
rename from merge.py
rename to merge-HF-and-lora-to-HF.py
index f881802a4..bd3db4609 100644
--- a/merge.py
+++ b/merge-HF-and-lora-to-HF.py
@@ -1,31 +1,31 @@
-import os, time
-import tempfile
 import json
 import torch
 import argparse
-import transformers
 from transformers import LlamaTokenizer, LlamaForCausalLM
 from peft import PeftModel
 
-# args
-parser = argparse.ArgumentParser()
+# args with description.
+parser = argparse.ArgumentParser(
+    prog="Merge HF file with Lora\n",
+    description="Please locate HF format model path with pytorch_*.bin inside, lora path with adapter_config.json and adapter_model.bin.",
+)
+
 # The original base model checkpoint dir
-parser.add_argument("--model_path", type=str, default='llama-7b-hf')
+parser.add_argument("--model_path", type=str, default="decapoda-research/llama-7b-hf", help="Directory contain original HF model")
 # The finetuned lora model checkpoint dir
-parser.add_argument("--lora_path",type=str, default='lora')
+parser.add_argument("--lora_path", type=str, default="decapoda-research/lora", help="Directory contain Lora ")
 # The output dir
-parser.add_argument("--out_path", type=str, default='lora-merged')
+parser.add_argument("--out_path", type=str, default="decapoda-research/lora-merged", help="Directory  store merged HF model")
 
 args = parser.parse_args()
 
-
-
 print(f">>> load model from {args.model_path} and lora from {args.lora_path}....")
 
+# transformer loaded. load and save Tokenizer.
 tokenizer = LlamaTokenizer.from_pretrained(args.model_path)
+tokenizer.save_pretrained(args.out_path)
 
-#transformer loaded. load  model.
-
+# load model.
 model = LlamaForCausalLM.from_pretrained(
     args.model_path,
     load_in_8bit=False,
@@ -34,7 +34,7 @@ model = LlamaForCausalLM.from_pretrained(
 )
 
 
-#peft loaded. load lora.
+# peft loaded. load lora.
 model = PeftModel.from_pretrained(
     model,
     args.lora_path,
@@ -44,7 +44,6 @@ model = PeftModel.from_pretrained(
 
 print(f">>> merging lora...")
 
-#Using new Peft function merge Lora
+# Using Peft function to merge Lora.
 model = model.merge_and_unload()
 model.save_pretrained(args.out_path)
-