diff --git a/examples/lightrag_lmdeploy_demo.py b/examples/lightrag_lmdeploy_demo.py
index ea7ace0e..aeb96f71 100644
--- a/examples/lightrag_lmdeploy_demo.py
+++ b/examples/lightrag_lmdeploy_demo.py
@@ -10,10 +10,11 @@ WORKING_DIR = "./dickens"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def lmdeploy_model_complete(
     prompt=None, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
-    model_name = kwargs["hashing_kv"].global_config["llm_model_name"] 
+    model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
     return await lmdeploy_model_if_cache(
         model_name,
         prompt,
@@ -23,7 +24,7 @@ async def lmdeploy_model_complete(
         ## or model_name is a pytorch model on huggingface.co,
         ## you can refer to https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/model.py
         ## for a list of chat_template available in lmdeploy.
-        chat_template = "llama3",
+        chat_template="llama3",
         # model_format ='awq', # if you are using awq quantization model.
         # quant_policy=8, # if you want to use online kv cache, 4=kv int4, 8=kv int8.
         **kwargs,
@@ -33,7 +34,7 @@ async def lmdeploy_model_complete(
 rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=lmdeploy_model_complete,
-    llm_model_name="meta-llama/Llama-3.1-8B-Instruct", # please use definite path for local model
+    llm_model_name="meta-llama/Llama-3.1-8B-Instruct",  # please use definite path for local model
     embedding_func=EmbeddingFunc(
         embedding_dim=384,
         max_token_size=5000,