diff --git a/README.md b/README.md
index f45a312b..e548709e 100644
--- a/README.md
+++ b/README.md
@@ -88,7 +88,7 @@ huggingface-cli login
 - 🤗Transformers, Datasets, Accelerate, PEFT and TRL
 - jieba, rouge_chinese and nltk (used at evaluation)
 - gradio and mdtex2html (used in web_demo.py)
-- uvicorn and fastapi (used in api_demo.py)
+- uvicorn, fastapi and sse_starlette (used in api_demo.py)
 
 And **powerful GPUs**!
 
diff --git a/requirements.txt b/requirements.txt
index f0fbdf2d..50a67c33 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,6 +4,7 @@ datasets>=2.12.0
 accelerate>=0.19.0
 peft>=0.3.0
 trl>=0.4.4
+sentencepiece
 jieba
 rouge_chinese
 nltk
@@ -11,4 +12,4 @@ gradio
 mdtex2html
 uvicorn
 fastapi
-sentencepiece
+sse_starlette
diff --git a/src/api_demo.py b/src/api_demo.py
index fd1d450a..425e89fe 100644
--- a/src/api_demo.py
+++ b/src/api_demo.py
@@ -13,7 +13,7 @@ from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from contextlib import asynccontextmanager
 from transformers import TextIteratorStreamer
-from starlette.responses import StreamingResponse
+from sse_starlette import EventSourceResponse
 from typing import Any, Dict, List, Literal, Optional, Union
 
 from utils import (
@@ -144,7 +144,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
 
     if request.stream:
         generate = predict(gen_kwargs, request.model)
-        return StreamingResponse(generate, media_type="text/event-stream")
+        return EventSourceResponse(generate, media_type="text/event-stream")
 
     generation_output = model.generate(**gen_kwargs)
     outputs = generation_output.tolist()[0][len(inputs["input_ids"][0]):]
@@ -174,7 +174,7 @@ async def predict(gen_kwargs: Dict[str, Any], model_id: str):
         finish_reason=None
     )
     chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
-    yield "data: {}\n\n".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+    yield chunk.json(exclude_unset=True, ensure_ascii=False)
 
     for new_text in streamer:
         if len(new_text) == 0:
@@ -186,7 +186,7 @@ async def predict(gen_kwargs: Dict[str, Any], model_id: str):
             finish_reason=None
         )
         chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
-        yield "data: {}\n\n".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+        yield chunk.json(exclude_unset=True, ensure_ascii=False)
 
     choice_data = ChatCompletionResponseStreamChoice(
         index=0,
@@ -194,7 +194,8 @@ async def predict(gen_kwargs: Dict[str, Any], model_id: str):
         finish_reason="stop"
     )
     chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
-    yield "data: {}\n\n".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+    yield chunk.json(exclude_unset=True, ensure_ascii=False)
+    yield "[DONE]"
 
 
 if __name__ == "__main__":
@@ -204,4 +205,4 @@ if __name__ == "__main__":
     prompt_template = Template(data_args.prompt_template)
     source_prefix = data_args.source_prefix if data_args.source_prefix else ""
 
-    uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
+    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)