diff --git a/examples/demo/openai_api.py b/examples/demo/openai_api.py
new file mode 100644
index 00000000..284d898e
--- /dev/null
+++ b/examples/demo/openai_api.py
@@ -0,0 +1,672 @@
+# coding=utf-8
+# Usage: python qwen1.5_openai_api.py
+# Visit http://localhost:8866/docs for documents.
+"""
+__project_ = 'Qwen1.5'
+__file_name__ = 'qwen1.5_openai_api'
+__author__ = 刘佳男
+__time__ = '2024/4/9'
+__product_name = PyCharm
+__Description :
+# code is far away from bugs with the god animal protecting
+    I love animals. They taste delicious.
+              ┏┓      ┏┓
+            ┏┛┻━━━┛┻┓
+            ┃        ┃
+            ┃  ┳┛  ┗┳  ┃
+            ┃      ┻      ┃
+            ┗━┓      ┏━┛
+                ┃      ┗━━━┓
+                ┃  神兽保佑    ┣┓
+                ┃　永无BUG！   ┏┛
+                ┗┓┓┏━┳┓┏┛
+                  ┃┫┫  ┃┫┫
+                  ┗┻┛  ┗┻┛
+"""
+
+import re
+import copy
+import json
+import time
+from argparse import ArgumentParser
+from contextlib import asynccontextmanager
+from typing import Dict, List, Literal, Optional, Union
+
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from sentence_transformers import SentenceTransformer
+from sklearn.preprocessing import PolynomialFeatures
+from sse_starlette.sse import EventSourceResponse
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+from transformers.generation import GenerationConfig
+import numpy as np
+import tiktoken
+
+from threading import Thread
+
+# 服务器路径
+# DEFAULT_CKPT_PATH = '../model1/Qwen-72B-Chat'
+# 本地路径
+DEFAULT_CKPT_PATH = 'E:\\Qwen1.5-0.5B-Chat'
+
+# 服务器路径
+# DEFAULT_M3E_LARGE = '../model1/m3e-large'
+# 本地路径
+DEFAULT_M3E_LARGE = 'E:\\m3e-large'
+
+
+def _gc(forced: bool = False):
+    global args
+    if args.disable_gc and not forced:
+        return
+
+    import gc
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):  # collects GPU memory
+    yield
+    _gc(forced=True)
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+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", "function"]
+    content: Optional[str]
+    function_call: Optional[Dict] = None
+
+
+class DeltaMessage(BaseModel):
+    role: Optional[Literal["user", "assistant", "system"]] = None
+    content: Optional[str] = None
+
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: List[ChatMessage]
+    functions: Optional[List[Dict]] = None
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    max_length: Optional[int] = None
+    stream: Optional[bool] = False
+    stop: Optional[List[str]] = None
+
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: Literal["stop", "length", "function_call"]
+
+
+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()))
+
+
+class EmbeddingRequest(BaseModel):
+    input: List[str]
+    model: str
+
+
+class EmbeddingResponse(BaseModel):
+    data: list
+    model: str
+    object: str
+    usage: dict
+
+def num_tokens_from_string(string: str) -> int:
+    """Returns the number of tokens in a text string."""
+    encoding = tiktoken.get_encoding('cl100k_base')
+    num_tokens = len(encoding.encode(string))
+    return num_tokens
+
+
+def expand_features(embedding, target_length):
+    poly = PolynomialFeatures(degree=2)
+    expanded_embedding = poly.fit_transform(embedding.reshape(1, -1))
+    expanded_embedding = expanded_embedding.flatten()
+    if len(expanded_embedding) > target_length:
+        # 如果扩展后的特征超过目标长度，可以通过截断或其他方法来减少维度
+        expanded_embedding = expanded_embedding[:target_length]
+    elif len(expanded_embedding) < target_length:
+        # 如果扩展后的特征少于目标长度，可以通过填充或其他方法来增加维度
+        expanded_embedding = np.pad(
+            expanded_embedding, (0, target_length - len(expanded_embedding))
+        )
+    return expanded_embedding
+
+@app.get("/v1/models", response_model=ModelList)
+async def list_models():
+    global model_args
+    model_card = ModelCard(id="gpt-3.5-turbo")
+    return ModelList(data=[model_card])
+
+
+# To work around that unpleasant leading-\n tokenization issue!
+def add_extra_stop_words(stop_words):
+    if stop_words:
+        _stop_words = []
+        _stop_words.extend(stop_words)
+        for x in stop_words:
+            s = x.lstrip("\n")
+            if s and (s not in _stop_words):
+                _stop_words.append(s)
+        return _stop_words
+    return stop_words
+
+
+def trim_stop_words(response, stop_words):
+    if stop_words:
+        for stop in stop_words:
+            idx = response.find(stop)
+            if idx != -1:
+                response = response[:idx]
+    return response
+
+
+TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters}"""
+
+REACT_INSTRUCTION = """Answer the following questions as best you can. You have access to the following APIs:
+
+{tools_text}
+
+Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [{tools_name_text}]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin!"""
+
+_TEXT_COMPLETION_CMD = object()
+
+
+#
+# Temporarily, the system role does not work as expected.
+# We advise that you write the setups for role-play in your query,
+# i.e., use the user role instead of the system role.
+#
+# TODO: Use real system role when the model is ready.
+#
+def parse_messages(messages, functions):
+    if all(m.role != "user" for m in messages):
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid request: Expecting at least one user message.",
+        )
+
+    messages = copy.deepcopy(messages)
+    default_system = "You are a helpful assistant."
+    system = ""
+    if messages[0].role == "system":
+        system = messages.pop(0).content.lstrip("\n").rstrip()
+        if system == default_system:
+            system = ""
+
+    if functions:
+        tools_text = []
+        tools_name_text = []
+        for func_info in functions:
+            name = func_info.get("name", "")
+            name_m = func_info.get("name_for_model", name)
+            name_h = func_info.get("name_for_human", name)
+            desc = func_info.get("description", "")
+            desc_m = func_info.get("description_for_model", desc)
+            tool = TOOL_DESC.format(
+                name_for_model=name_m,
+                name_for_human=name_h,
+                # Hint: You can add the following format requirements in description:
+                #   "Format the arguments as a JSON object."
+                #   "Enclose the code within triple backticks (`) at the beginning and end of the code."
+                description_for_model=desc_m,
+                parameters=json.dumps(func_info["parameters"], ensure_ascii=False),
+            )
+            tools_text.append(tool)
+            tools_name_text.append(name_m)
+        tools_text = "\n\n".join(tools_text)
+        tools_name_text = ", ".join(tools_name_text)
+        system += "\n\n" + REACT_INSTRUCTION.format(
+            tools_text=tools_text,
+            tools_name_text=tools_name_text,
+        )
+        system = system.lstrip("\n").rstrip()
+
+    dummy_thought = {
+        "en": "\nThought: I now know the final answer.\nFinal answer: ",
+        "zh": "\nThought: 我会作答了。\nFinal answer: ",
+    }
+
+    _messages = messages
+    messages = []
+    for m_idx, m in enumerate(_messages):
+        role, content, func_call = m.role, m.content, m.function_call
+        if content:
+            content = content.lstrip("\n").rstrip()
+        if role == "function":
+            if (len(messages) == 0) or (messages[-1].role != "assistant"):
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Invalid request: Expecting role assistant before role function.",
+                )
+            messages[-1].content += f"\nObservation: {content}"
+            if m_idx == len(_messages) - 1:
+                messages[-1].content += "\nThought:"
+        elif role == "assistant":
+            if len(messages) == 0:
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Invalid request: Expecting role user before role assistant.",
+                )
+            last_msg = messages[-1].content
+            last_msg_has_zh = len(re.findall(r"[\u4e00-\u9fff]+", last_msg)) > 0
+            if func_call is None:
+                if functions:
+                    content = dummy_thought["zh" if last_msg_has_zh else "en"] + content
+            else:
+                f_name, f_args = func_call["name"], func_call["arguments"]
+                if not content:
+                    if last_msg_has_zh:
+                        content = f"Thought: 我可以使用 {f_name} API。"
+                    else:
+                        content = f"Thought: I can use {f_name}."
+                content = f"\n{content}\nAction: {f_name}\nAction Input: {f_args}"
+            if messages[-1].role == "user":
+                messages.append(
+                    ChatMessage(role="assistant", content=content.lstrip("\n").rstrip())
+                )
+            else:
+                messages[-1].content += content
+        elif role == "user":
+            messages.append(
+                ChatMessage(role="user", content=content.lstrip("\n").rstrip())
+            )
+        else:
+            raise HTTPException(
+                status_code=400, detail=f"Invalid request: Incorrect role {role}."
+            )
+
+    query = _TEXT_COMPLETION_CMD
+    if messages[-1].role == "user":
+        query = messages[-1].content
+        messages = messages[:-1]
+
+    if len(messages) % 2 != 0:
+        raise HTTPException(status_code=400, detail="Invalid request")
+
+    history = []  # [(Q1, A1), (Q2, A2), ..., (Q_last_turn, A_last_turn)]
+    for i in range(0, len(messages), 2):
+        if messages[i].role == "user" and messages[i + 1].role == "assistant":
+            usr_msg = messages[i].content.lstrip("\n").rstrip()
+            bot_msg = messages[i + 1].content.lstrip("\n").rstrip()
+            if system and (i == len(messages) - 2):
+                usr_msg = f"{system}\n\nQuestion: {usr_msg}"
+                system = ""
+            for t in dummy_thought.values():
+                t = t.lstrip("\n")
+                if bot_msg.startswith(t) and ("\nAction: " in bot_msg):
+                    bot_msg = bot_msg[len(t) :]
+            history.append([usr_msg, bot_msg])
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail="Invalid request: Expecting exactly one user (or function) role before every assistant role.",
+            )
+    if system:
+        assert query is not _TEXT_COMPLETION_CMD
+        query = f"{system}\n\nQuestion: {query}"
+    return query, history
+
+
+def parse_response(response):
+    func_name, func_args = "", ""
+    i = response.rfind("\nAction:")
+    j = response.rfind("\nAction Input:")
+    k = response.rfind("\nObservation:")
+    if 0 <= i < j:  # If the text has `Action` and `Action input`,
+        if k < j:  # but does not contain `Observation`,
+            # then it is likely that `Observation` is omitted by the LLM,
+            # because the output text may have discarded the stop word.
+            response = response.rstrip() + "\nObservation:"  # Add it back.
+        k = response.rfind("\nObservation:")
+        func_name = response[i + len("\nAction:") : j].strip()
+        func_args = response[j + len("\nAction Input:") : k].strip()
+    if func_name:
+        choice_data = ChatCompletionResponseChoice(
+            index=0,
+            message=ChatMessage(
+                role="assistant",
+                content=response[:i],
+                function_call={"name": func_name, "arguments": func_args},
+            ),
+            finish_reason="function_call",
+        )
+        return choice_data
+    z = response.rfind("\nFinal Answer: ")
+    if z >= 0:
+        response = response[z + len("\nFinal Answer: ") :]
+    choice_data = ChatCompletionResponseChoice(
+        index=0,
+        message=ChatMessage(role="assistant", content=response),
+        finish_reason="stop",
+    )
+    return choice_data
+
+
+# completion mode, not chat mode
+def text_complete_last_message(history, stop_words_ids, gen_kwargs):
+    im_start = "<|im_start|>"
+    im_end = "<|im_end|>"
+    prompt = f"{im_start}system\nYou are a helpful assistant.{im_end}"
+    for i, (query, response) in enumerate(history):
+        query = query.lstrip("\n").rstrip()
+        response = response.lstrip("\n").rstrip()
+        prompt += f"\n{im_start}user\n{query}{im_end}"
+        prompt += f"\n{im_start}assistant\n{response}{im_end}"
+    prompt = prompt[: -len(im_end)]
+
+    _stop_words_ids = [tokenizer.encode(im_end)]
+    if stop_words_ids:
+        for s in stop_words_ids:
+            _stop_words_ids.append(s)
+    stop_words_ids = _stop_words_ids
+
+    input_ids = torch.tensor([tokenizer.encode(prompt)]).to(model.device)
+    output = model.generate(input_ids, stop_words_ids=stop_words_ids, **gen_kwargs).tolist()[0]
+    output = tokenizer.decode(output, errors="ignore")
+    assert output.startswith(prompt)
+    output = output[len(prompt) :]
+    output = trim_stop_words(output, ["<|endoftext|>", im_end])
+    print(f"<completion>\n{prompt}\n<!-- *** -->\n{output}\n</completion>")
+    return output
+
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def create_chat_completion(request: ChatCompletionRequest):
+    global model, tokenizer
+
+    gen_kwargs = {}
+    if request.temperature is not None:
+        if request.temperature < 0.01:
+            gen_kwargs['top_k'] = 1  # greedy decoding
+        else:
+            # Not recommended. Please tune top_p instead.
+            gen_kwargs['temperature'] = request.temperature
+    if request.top_p is not None:
+        gen_kwargs['top_p'] = request.top_p
+
+    stop_words = add_extra_stop_words(request.stop)
+    if request.functions:
+        stop_words = stop_words or []
+        if "Observation:" not in stop_words:
+            stop_words.append("Observation:")
+
+    query, history = parse_messages(request.messages, request.functions)
+
+    if request.stream:
+        if request.functions:
+            raise HTTPException(
+                status_code=400,
+                detail="Invalid request: Function calling is not yet implemented for stream mode.",
+            )
+        generate = predict(query, history, request.model, stop_words, gen_kwargs)
+        return EventSourceResponse(generate, media_type="text/event-stream")
+
+    stop_words_ids = [tokenizer.encode(s) for s in stop_words] if stop_words else None
+    if query is _TEXT_COMPLETION_CMD:
+        response = text_complete_last_message(history, stop_words_ids=stop_words_ids, gen_kwargs=gen_kwargs)
+    else:
+        response, _ = model.chat(
+            tokenizer,
+            query,
+            history=history,
+            stop_words_ids=stop_words_ids,
+            **gen_kwargs
+        )
+        print(f"<chat>\n{history}\n{query}\n<!-- *** -->\n{response}\n</chat>")
+    _gc()
+
+    response = trim_stop_words(response, stop_words)
+    if request.functions:
+        choice_data = parse_response(response)
+    else:
+        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"
+    )
+
+
+def _dump_json(data: BaseModel, *args, **kwargs) -> str:
+    try:
+        return data.model_dump_json(*args, **kwargs)
+    except AttributeError:  # pydantic<2.0.0
+        return data.json(*args, **kwargs)  # noqa
+
+def _chat_stream(model, tokenizer, query, history):
+    conversation = [
+        {'role': 'system', 'content': 'You are a helpful assistant.'},
+    ]
+    for query_h, response_h in history:
+        conversation.append({'role': 'user', 'content': query_h})
+        conversation.append({'role': 'assistant', 'content': response_h})
+    conversation.append({'role': 'user', 'content': query})
+    inputs = tokenizer.apply_chat_template(
+        conversation,
+        add_generation_prompt=True,
+        return_tensors='pt',
+    )
+    inputs = inputs.to(model.device)
+    streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True)
+    generation_kwargs = dict(
+        input_ids=inputs,
+        streamer=streamer,
+    )
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    for new_text in streamer:
+        # print(new_text)
+        yield new_text
+
+
+async def predict(
+    query: str, history: List[List[str]], model_id: str, stop_words: List[str], gen_kwargs: Dict,
+):
+    global model, tokenizer
+    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 "{}".format(_dump_json(chunk, exclude_unset=True))
+
+    current_length = 0
+    stop_words_ids = [tokenizer.encode(s) for s in stop_words] if stop_words else None
+    if stop_words:
+        # TODO: It's a little bit tricky to trim stop words in the stream mode.
+        raise HTTPException(
+            status_code=400,
+            detail="Invalid request: custom stop words are not yet supported for stream mode.",
+        )
+    # response_generator = model.chat_stream(
+    #     tokenizer, query, history=history, stop_words_ids=stop_words_ids, **gen_kwargs
+    # )
+
+    # response_generator = _chat_stream(model, tokenizer, query, history=history)
+
+    for new_response in _chat_stream(model, tokenizer, query, history=history):
+        # if len(new_response) == current_length:
+        #     continue
+        if len(new_response) == current_length:
+            continue
+
+        new_text = new_response
+        # print(new_text)
+        # new_text = new_response[current_length:]
+        # current_length = len(new_response)
+
+        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 "{}".format(_dump_json(chunk, exclude_unset=True))
+
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0, delta=DeltaMessage(), finish_reason="stop"
+    )
+    chunk = ChatCompletionResponse(
+        model=model_id, choices=[choice_data], object="chat.completion.chunk"
+    )
+    yield "{}".format(_dump_json(chunk, exclude_unset=True))
+    yield "[DONE]"
+
+    _gc()
+
+
+@app.post("/v1/embeddings", response_model=EmbeddingResponse)
+async def get_embeddings(request: EmbeddingRequest):
+    # 计算嵌入向量和tokens数量
+    embeddings = [embeddings_model.encode(text) for text in request.input]
+
+    # 如果嵌入向量的维度不为1536，则使用插值法扩展至1536维度
+    embeddings = [
+        expand_features(embedding, 1536) if len(embedding) < 1536 else embedding
+        for embedding in embeddings
+    ]
+
+    # Min-Max normalization 归一化
+    embeddings = [embedding / np.linalg.norm(embedding) for embedding in embeddings]
+
+    # 将numpy数组转换为列表
+    embeddings = [embedding.tolist() for embedding in embeddings]
+    prompt_tokens = sum(len(text.split()) for text in request.input)
+    total_tokens = sum(num_tokens_from_string(text) for text in request.input)
+
+    response = {
+        "data": [
+            {"embedding": embedding, "index": index, "object": "embedding"}
+            for index, embedding in enumerate(embeddings)
+        ],
+        "model": request.model,
+        "object": "list",
+        "usage": {
+            "prompt_tokens": prompt_tokens,
+            "total_tokens": total_tokens,
+        },
+    }
+
+    return response
+
+
+def _get_args():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--checkpoint-path",
+        type=str,
+        default=DEFAULT_CKPT_PATH,
+        help="Checkpoint name or path, default to %(default)r",
+    )
+    parser.add_argument(
+        "--cpu-only", action="store_true", help="Run demo with CPU only"
+    )
+    parser.add_argument(
+        "--server-port", type=int, default=8866, help="Demo server port."
+    )
+    parser.add_argument(
+        "--server-name",
+        type=str,
+        default="0.0.0.0",
+        help="Demo server name. Default: 127.0.0.1, which is only visible from the local computer."
+        " If you want other computers to access your server, use 0.0.0.0 instead.",
+    )
+    parser.add_argument("--disable-gc", action="store_true",
+                        help="Disable GC after each response generated.")
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = _get_args()
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.checkpoint_path,
+        trust_remote_code=True,
+        resume_download=True,
+    )
+
+    if args.cpu_only:
+        device_map = "cpu"
+    else:
+        device_map = "auto"
+
+    model = AutoModelForCausalLM.from_pretrained(
+        args.checkpoint_path,
+        device_map=device_map,
+        trust_remote_code=True,
+        resume_download=True,
+    ).eval()
+
+    model.generation_config = GenerationConfig.from_pretrained(
+        args.checkpoint_path,
+        trust_remote_code=True,
+        resume_download=True,
+    )
+    model.generation_config.max_new_tokens = 4096
+
+    embeddings_model = SentenceTransformer(DEFAULT_M3E_LARGE, device='cpu')
+
+    uvicorn.run(app, host=args.server_name, port=args.server_port, workers=1)