QwQ-32B，支持Function Call的推理模型，深度思考Agent的时代来了！

近期，Qwen 发布了 QwQ-32B - 一个在许多基准测试中性能可与 DeepSeek-R1 相媲美的推理模型。QwQ在推理模型中集成了调用工具的能力，使其能够在使用工具的同时进行批判性思考，并根据反馈调整推理过程。这样的能力使得QwQ能够很好在Agentic System中使用。本文介绍如何通过vLLM和SgLang结合QwQ-32B，搭建OpenAI格式的聊天API，并与外部函数结合来拓展

健忘的派大星

1044人浏览 · 2025-04-20 08:00:00

健忘的派大星 · 2025-04-20 08:00:00 发布

前言

tools是OpenAI的Chat Completion API中的一个可选参数，可用于提供函数调用规范（function specifications）。这样做的目的是使模型能够生成符合所提供的规范的函数参数格式。同时，API 实际上不会执行任何函数调用。开发人员需要使用模型输出来执行函数调用。

vLLM和SgLang均支持OpenAI-API的tool参数。通过tool参数以及其中的函数调用规范，QwQ将能决定何时调用什么样的函数，以及怎么调用函数。

注：本文测试用例参考OpenAI cookbook：https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models

本文主要包含以下两个个部分：

模型部署：使用vLLM，SgLang和QwQ，通过设置参数，部署支持Function call的聊天API接口。
生成函数参数：指定一组函数并使用 API 生成函数参数。

1、模型部署

模型文件下载

modelscope download --model=Qwen/QwQ-32B --local_dir ./QwQ-32B

环境安装

pip install vllm
pip install "sglang[all]>=0.4.3.post2"

vLLM部署命令

vllm serve /ModelPath/QwQ-32B \
--port 8000 \
--reasoning-parser deepseek_r1 \
--max_model_len 4096 \
--enable-auto-tool-choice \
--tool-call-parser hermes

sglang部署命令

python -m sglang.launch_server --model-path /ModelPath/QwQ-32B --port 3001 --host 0.0.0.0 --tool-call-parser qwen25

模型调用

使用OpenAI的API格式调用本地部署的QwQ模型

单轮对话

from openai import OpenAI
 
# 设置 OpenAI 的 API 密钥和 API 基础 URL 使用 vLLM 的 API 服务器。
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
 
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
 
# 使用流式输出（stream=True）
chat_response = client.chat.completions.create(
    model="path/to/QwQ-32B",
    messages=[{"role": "user", "content": "你好"}],
    stream=True  # 启用流式响应
)
 
# 处理流式输出
contents = []
for e in chat_response:
    # print(e.choices[0].delta.content,end="")
    contents.append(e.choices[0].delta.content)
print("".join(contents))

多轮对话

from openai import OpenAI
import os

# 初始化OpenAI客户端
client = OpenAI(
    api_key = "empty",
    base_url="http://localhost:8000/v1"
)

reasoning_content = ""  # 定义完整思考过程
answer_content = ""     # 定义完整回复
is_answering = False   # 判断是否结束思考过程并开始回复

messages = []
conversation_idx = 1
while True:
    print("="*20+f"第{conversation_idx}轮对话"+"="*20)
    conversation_idx += 1
    user_msg = {"role": "user", "content": input("请输入你的消息：")}
    messages.append(user_msg)
    # 创建聊天完成请求
    completion = client.chat.completions.create(
       model="path/to/QwQ-32B",# 此处以 qwq-32b 为例，可按需更换模型名称
        messages=messages,
        stream=True
    )
    print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
    for chunk in completion:
        # 如果chunk.choices为空，则打印usage
        if not chunk.choices:
            print("\nUsage:")
            print(chunk.usage)
        else:
            delta = chunk.choices[0].delta
            # 打印思考过程
            if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
                print(delta.reasoning_content, end='', flush=True)
                reasoning_content += delta.reasoning_content
            else:
                # 开始回复
                if delta.content != "" and is_answering is False:
                    print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
                    is_answering = True
                # 打印回复过程
                print(delta.content, end='', flush=True)
                answer_content += delta.content
    messages.append({"role": "assistant", "content": answer_content})
    print("\n")
    # print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
    # print(reasoning_content)
    # print("=" * 20 + "完整回复" + "=" * 20 + "\n")
    # print(answer_content)

2、使用工具

首先，定义模型调用函数

from openai import OpenAI
 
# 设置 OpenAI 的 API 密钥和 API 基础 URL 使用 vLLM 的 API 服务器。
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
MODEL = "path/to/QwQ-32B"
 
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

def chat_completion_request(messages, tools=None, tool_choice=None, model=MODEL):
    try:
        response = client.chat.completions.create(
            model=model,
            messages=messages,
            tools=tools,
            tool_choice="auto",
        )
        return response
    except Exception as e:
        print("Unable to generate ChatCompletion response")
        print(f"Exception: {e}")
        raise

然后，我们定义一些实用工具，用于调用聊天完成 API 以及维护和跟踪对话状态。

def pretty_print_conversation(messages):
    role_to_color = {
        "system": "red",
        "user": "green",
        "assistant": "blue",
        "function": "magenta",
    }
    
    for message in messages:
        if message["role"] == "system":
            print(colored(f"system: {message['content']}\n", role_to_color[message["role"]]))
        elif message["role"] == "user":
            print(colored(f"user: {message['content']}\n", role_to_color[message["role"]]))
        elif message["role"] == "assistant" and message.get("function_call"):
            print(colored(f"assistant: {message['function_call']}\n", role_to_color[message["role"]]))
        elif message["role"] == "assistant" and not message.get("function_call"):
            print(colored(f"assistant: {message['content']}\n", role_to_color[message["role"]]))
        elif message["role"] == "function":
            print(colored(f"function ({message['name']}): {message['content']}\n", role_to_color[message["role"]]))

3、工具定义

这里假设了一个天气 API，并设置了一些函数规范和它进行交互。将这些函数规范传递给 Chat API，以便模型可以生成符合规范的函数参数。

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use. Infer this from the users location.",
                    },
                },
                "required": ["location", "format"],
            },
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_n_day_weather_forecast",
            "description": "Get an N-day weather forecast",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use. Infer this from the users location.",
                    },
                    "num_days": {
                        "type": "integer",
                        "description": "The number of days to forecast",
                    }
                },
                "required": ["location", "format", "num_days"]
            },
        }
    },
]

如果我们向模型询问当前的天气情况，它将会反问，希望获取到进一步的更多的参数信息。

messages = []
messages.append({"role": "user", "content": "hi ，can you tell me what's the weather like today"})
chat_response = chat_completion_request(
    messages, tools=tools
)
print(chat_response)
assistant_message = chat_response.choices[0].message
messages.append(assistant_message)
assistant_message

一旦我们通过对话提供缺失的参数信息，模型就会为我们生成适当的函数参数。

messages.append({"role": "user", "content": "I'm in Glasgow, Scotland."})
chat_response = chat_completion_request(
    messages, tools=tools
)
assistant_message = chat_response.choices[0].message
messages.append(assistant_message)
assistant_message

通过不同的提示词，我们可以让它反问不同的问题以获取函数参数信息。

messages = []
messages.append({"role": "user", "content": "can you tell me, what is the weather going to be like in Glasgow, Scotland in next x days"})
chat_response = chat_completion_request(
    messages, tools=tools
)
assistant_message = chat_response.choices[0].message
messages.append(assistant_message)
assistant_message

messages.append({"role": "user", "content": "5 days"})
chat_response = chat_completion_request(
    messages, tools=tools
)
chat_response.choices[0]

并行函数调用

支持一次提问中，并行调用多次函数

messages = []
messages.append({"role": "user", "content": "what is the weather going to be like in San Francisco and Glasgow over the next 4 days"})
chat_response = chat_completion_request(
    messages, tools=tools, model=MODEL
)

assistant_message = chat_response.choices[0].message.tool_calls
assistant_message