diff --git a/openai_api.py b/openai_api.py
new file mode 100644
index 0000000..1c1f63c
--- /dev/null
+++ b/openai_api.py
@@ -0,0 +1,244 @@
+r"""
+Modified from https://github.com/THUDM/ChatGLM-6B/blob/main/api.py
+Usage Example:
+```python
+import openai
+if __name__ == "__main__":
+    openai.api_base = "http://localhost:8000/v1"
+    openai.api_key = "none"
+    for chunk in openai.ChatCompletion.create(
+        model="chatglm2-6b",
+        messages=[
+            {"role": "user", "content": "你好"}
+        ],
+        stream=True
+    ):
+        if hasattr(chunk.choices[0].delta, "content"):
+            print(chunk.choices[0].delta.content, end="", flush=True)
+```
+"""
+
+import time
+import sys
+from threading import Thread
+from typing import Optional, List, Literal, Union
+
+import torch
+import uvicorn
+from pydantic import BaseModel, Field
+from loguru import logger as _logger
+from fastapi.responses import StreamingResponse
+from fastapi import FastAPI, HTTPException
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers.generation.streamers import TextIteratorStreamer
+
+
+def define_log_level(print_level="INFO", logfile_level="DEBUG"):
+    _logger.remove()
+    _logger.add(sys.stderr, level=print_level)
+    # _logger.add('logs/log.txt', level=logfile_level)
+    return _logger
+
+
+logger = define_log_level()
+
+
+class ModelCard(BaseModel):
+    id: str
+    object: str = "model"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    owned_by: str = "owner"
+    root: Optional[str] = None
+    parent: Optional[str] = None
+    permission: Optional[list] = None
+
+
+class ModelList(BaseModel):
+    object: str = "list"
+    data: List[ModelCard] = []
+
+
+class ChatMessage(BaseModel):
+    role: Literal["user", "assistant", "system"]
+    content: str
+
+
+class DeltaMessage(BaseModel):
+    role: Optional[Literal["user", "assistant", "system"]] = None
+    content: Optional[str] = None
+
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: List[ChatMessage]
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    max_length: Optional[int] = None
+    stream: Optional[bool] = False
+
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: Literal["stop", "length"]
+
+
+class ChatCompletionResponseStreamChoice(BaseModel):
+    index: int
+    delta: DeltaMessage
+    finish_reason: Optional[Literal["stop", "length"]]
+
+
+class ChatCompletionResponse(BaseModel):
+    model: str
+    object: Literal["chat.completion", "chat.completion.chunk"]
+    choices: List[
+        Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]
+    ]
+    created: Optional[int] = Field(default_factory=lambda: int(time.time()))
+
+
+# def torch_gc():
+#     if torch.cuda.is_available():
+#         torch.cuda.empty_cache()
+#         torch.cuda.ipc_collect()
+
+
+app = FastAPI()
+
+B_INST, E_INST = "[INST]", "[/INST]"
+SYSTEM = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
+
+
+async def predict(streamer: TextIteratorStreamer):
+    model_id = "llama2"
+
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0, delta=DeltaMessage(role="assistant"), 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))
+
+    for new_text in streamer:
+        if not new_text:
+            continue
+        choice_data = ChatCompletionResponseStreamChoice(
+            index=0, delta=DeltaMessage(content=new_text), 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))
+
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0, delta=DeltaMessage(), 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))
+
+
+@app.get("/v1/models", response_model=ModelList)
+async def list_models():
+    model_card = ModelCard(id="gpt-3.5-turbo")
+    return ModelList(data=[model_card])
+
+
+def get_prompt(
+    message: str, chat_history: list[tuple[str, str]], system_prompt: str
+) -> str:
+    texts = [f"<s>{B_INST} <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"]
+
+    # The first user input is _not_ stripped
+    do_strip = False
+    for user_input, response in chat_history:
+        user_input = user_input.strip() if do_strip else user_input
+        do_strip = True
+        texts.append(f"{user_input} {E_INST} {response.strip()} </s><s>{B_INST} ")
+
+    message = message.strip() if do_strip else message
+    texts.append(f"{message} {E_INST}")
+    return "".join(texts)
+
+
+@app.post("/v1/chat/completions")
+async def create_item(request: ChatCompletionRequest):
+    global model, tokenizer
+
+    # 1. Build input_ids for LLM
+    if request.messages[-1].role != "user":
+        raise HTTPException(status_code=400, detail="Invalid request")
+    query = request.messages[-1].content
+    prev_messages = request.messages[:-1]
+
+    system = SYSTEM
+    if len(prev_messages) > 0 and prev_messages[0].role == "system":
+        system = prev_messages.pop(0).content
+
+    history = []
+    if len(prev_messages) % 2 == 0:
+        for i in range(0, len(prev_messages), 2):
+            if (
+                prev_messages[i].role == "user"
+                and prev_messages[i + 1].role == "assistant"
+            ):
+                history.append((prev_messages[i].content, prev_messages[i + 1].content))
+
+    prompt = get_prompt(query, history, system)
+    input_ids = tokenizer.encode(prompt, add_special_tokens=False)
+
+    logger.info(tokenizer.decode(input_ids))
+
+    # 2. Build the options dict for prediction
+    max_length = request.max_length or 4096
+    top_p = request.top_p or 0.7
+    temperature = request.temperature or 0.95
+    is_stream = request.stream
+
+    generation_kwargs = {
+        "inputs": torch.tensor([input_ids]).cuda(),
+        "max_length": max_length,
+        "top_p": top_p,
+        "temperature": temperature,
+    }
+
+    if is_stream:
+        streamer = TextIteratorStreamer(
+            tokenizer, skip_prompt=True, skip_special_tokens=True
+        )
+        generation_kwargs['streamer'] = streamer
+
+        thread = Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+        generate = predict(streamer)
+        return StreamingResponse(generate, media_type="text/event-stream")
+
+    else:
+        response = model.generate(**generation_kwargs)
+        response = tokenizer.decode(response[0, len(input_ids):], skip_special_tokens=True)
+        choice_data = ChatCompletionResponseChoice(
+            index=0,
+            message=ChatMessage(role="assistant", content=response),
+            finish_reason="stop"
+        )
+
+        return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")
+
+
+if __name__ == "__main__":
+    model_path = "LinkSoul/Chinese-Llama-2-7b"
+    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        load_in_4bit=model_path.endswith("4bit"),
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+    model.eval()
+
+    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)