都是自己的操作截图，手把手教学，零基础也15分钟可以本地调用自己的部署Qwen3大模型！！！

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系列文章目录

第一章 一文彻底搞定从0到1手把手教你部署Qwen3大模型

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目录

前言

一、安装gradio环境

二、使用demo代码调用大模型

1.进入指定demo目录

2.替换两个demo文件

三、运行CLI命令行demo

四、运行 Web demo

1.开启服务demo

2.下载Autodl平台SSH工具

3.通过SSH连接远程服务端口

---

前言

这次主要是通过两个Demo来是我们 本地调用 上期部署的Qwen3大模型，预计 15 分钟。

---

一、安装gradio环境

pip install gradio

二、使用demo代码调用大模型

1.进入指定demo目录

cd

cd autodl-tmp/Qwen3/examples/demo

2.替换两个demo文件

双击右边两个python文件，用下面两个代码依次替换掉，Ctrl+S即可保存

# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""A simple command-line interactive chat demo."""

import argparse
import os
import platform
import shutil
from copy import deepcopy
from threading import Thread

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from transformers.trainer_utils import set_seed

DEFAULT_CKPT_PATH = "/root/autodl-tmp/Qwen/Qwen3-8B"

_WELCOME_MSG = """\
Welcome to use Qwen3-8B model, type text to start chat, type :h to show command help.
(欢迎使用 Qwen3-8B-Instruct 模型，输入内容即可进行对话，:h 显示命令帮助。)

Note: This demo is governed by the original license of Qwen3-8B.
We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, including hate speech, violence, pornography, deception, etc.
(注：本演示受Qwen3-8B的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)
"""
_HELP_MSG = """\
Commands:
    :help / :h              Show this help message              显示帮助信息
    :exit / :quit / :q      Exit the demo                       退出Demo
    :clear / :cl            Clear screen                        清屏
    :clear-history / :clh   Clear history                       清除对话历史
    :history / :his         Show history                        显示对话历史
    :seed                   Show current random seed            显示当前随机种子
    :seed <N>               Set random seed to <N>              设置随机种子
    :conf                   Show current generation config      显示生成配置
    :conf <key>=<value>     Change generation config            修改生成配置
    :reset-conf             Reset generation config             重置生成配置
"""
_ALL_COMMAND_NAMES = [
    "help",
    "h",
    "exit",
    "quit",
    "q",
    "clear",
    "cl",
    "clear-history",
    "clh",
    "history",
    "his",
    "seed",
    "conf",
    "reset-conf",
]


def _setup_readline():
    try:
        import readline
    except ImportError:
        return

    _matches = []

    def _completer(text, state):
        nonlocal _matches

        if state == 0:
            _matches = [
                cmd_name for cmd_name in _ALL_COMMAND_NAMES if cmd_name.startswith(text)
            ]
        if 0 <= state < len(_matches):
            return _matches[state]
        return None

    readline.set_completer(_completer)
    readline.parse_and_bind("tab: complete")


def _load_model_tokenizer(args):
    tokenizer = AutoTokenizer.from_pretrained(
        args.checkpoint_path,
        resume_download=True,
    )

    if args.cpu_only:
        device_map = "cpu"
    else:
        device_map = "auto"

    model = AutoModelForCausalLM.from_pretrained(
        args.checkpoint_path,
        torch_dtype="auto",
        device_map=device_map,
        resume_download=True,
    ).eval()
    model.generation_config.max_new_tokens = 2048  # For chat.

    return model, tokenizer


def _gc():
    import gc

    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()


def _clear_screen():
    if platform.system() == "Windows":
        os.system("cls")
    else:
        os.system("clear")


def _print_history(history):
    terminal_width = shutil.get_terminal_size()[0]
    print(f"History ({len(history)})".center(terminal_width, "="))
    for index, (query, response) in enumerate(history):
        print(f"User[{index}]: {query}")
        print(f"Qwen[{index}]: {response}")
    print("=" * terminal_width)


def _get_input() -> str:
    while True:
        try:
            message = input("User> ").strip()
        except UnicodeDecodeError:
            print("[ERROR] Encoding error in input")
            continue
        except KeyboardInterrupt:
            exit(1)
        if message:
            return message
        print("[ERROR] Query is empty")


def _chat_stream(model, tokenizer, query, history):
    conversation = []
    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})
    input_text = tokenizer.apply_chat_template(
        conversation,
        add_generation_prompt=True,
        tokenize=False,
    )
    inputs = tokenizer([input_text], return_tensors="pt").to(model.device)
    streamer = TextIteratorStreamer(
        tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True
    )
    generation_kwargs = {
        **inputs,
        "streamer": streamer,
    }
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    for new_text in streamer:
        yield new_text


def main():
    parser = argparse.ArgumentParser(
        description="QwenQwen3-8B command-line interactive chat demo."
    )
    parser.add_argument(
        "-c",
        "--checkpoint-path",
        type=str,
        default=DEFAULT_CKPT_PATH,
        help="Checkpoint name or path, default to %(default)r",
    )
    parser.add_argument("-s", "--seed", type=int, default=1234, help="Random seed")
    parser.add_argument(
        "--cpu-only", action="store_true", help="Run demo with CPU only"
    )
    args = parser.parse_args()

    history, response = [], ""

    model, tokenizer = _load_model_tokenizer(args)
    orig_gen_config = deepcopy(model.generation_config)

    _setup_readline()

    _clear_screen()
    print(_WELCOME_MSG)

    seed = args.seed

    while True:
        query = _get_input()

        # Process commands.
        if query.startswith(":"):
            command_words = query[1:].strip().split()
            if not command_words:
                command = ""
            else:
                command = command_words[0]

            if command in ["exit", "quit", "q"]:
                break
            elif command in ["clear", "cl"]:
                _clear_screen()
                print(_WELCOME_MSG)
                _gc()
                continue
            elif command in ["clear-history", "clh"]:
                print(f"[INFO] All {len(history)} history cleared")
                history.clear()
                _gc()
                continue
            elif command in ["help", "h"]:
                print(_HELP_MSG)
                continue
            elif command in ["history", "his"]:
                _print_history(history)
                continue
            elif command in ["seed"]:
                if len(command_words) == 1:
                    print(f"[INFO] Current random seed: {seed}")
                    continue
                else:
                    new_seed_s = command_words[1]
                    try:
                        new_seed = int(new_seed_s)
                    except ValueError:
                        print(
                            f"[WARNING] Fail to change random seed: {new_seed_s!r} is not a valid number"
                        )
                    else:
                        print(f"[INFO] Random seed changed to {new_seed}")
                        seed = new_seed
                    continue
            elif command in ["conf"]:
                if len(command_words) == 1:
                    print(model.generation_config)
                else:
                    for key_value_pairs_str in command_words[1:]:
                        eq_idx = key_value_pairs_str.find("=")
                        if eq_idx == -1:
                            print("[WARNING] format: <key>=<value>")
                            continue
                        conf_key, conf_value_str = (
                            key_value_pairs_str[:eq_idx],
                            key_value_pairs_str[eq_idx + 1 :],
                        )
                        try:
                            conf_value = eval(conf_value_str)
                        except Exception as e:
                            print(e)
                            continue
                        else:
                            print(
                                f"[INFO] Change config: model.generation_config.{conf_key} = {conf_value}"
                            )
                            setattr(model.generation_config, conf_key, conf_value)
                continue
            elif command in ["reset-conf"]:
                print("[INFO] Reset generation config")
                model.generation_config = deepcopy(orig_gen_config)
                print(model.generation_config)
                continue
            else:
                # As normal query.
                pass

        # Run chat.
        set_seed(seed)
        _clear_screen()
        print(f"\nUser: {query}")
        print(f"\nQwen: ", end="")
        try:
            partial_text = ""
            for new_text in _chat_stream(model, tokenizer, query, history):
                print(new_text, end="", flush=True)
                partial_text += new_text
            response = partial_text
            print()

        except KeyboardInterrupt:
            print("[WARNING] Generation interrupted")
            continue

        history.append((query, response))


if __name__ == "__main__":
    main()

# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""A simple web interactive chat demo based on gradio."""

from argparse import ArgumentParser
from threading import Thread

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

DEFAULT_CKPT_PATH = "/root/autodl-tmp/Qwen/Qwen3-8B"


def _get_args():
    parser = ArgumentParser(description="Qwen3-8B web chat demo.")
    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(
        "--share",
        action="store_true",
        default=False,
        help="Create a publicly shareable link for the interface.",
    )
    parser.add_argument(
        "--inbrowser",
        action="store_true",
        default=False,
        help="Automatically launch the interface in a new tab on the default browser.",
    )
    parser.add_argument(
        "--server-port", type=int, default=6006, help="Demo server port."
    )
    parser.add_argument(
        "--server-name", type=str, default="127.0.0.1", help="Demo server name."
    )

    args = parser.parse_args()
    return args


def _load_model_tokenizer(args):
    tokenizer = AutoTokenizer.from_pretrained(
        args.checkpoint_path,
        resume_download=True,
    )

    if args.cpu_only:
        device_map = "cpu"
    else:
        device_map = "auto"

    model = AutoModelForCausalLM.from_pretrained(
        args.checkpoint_path,
        torch_dtype="auto",
        device_map=device_map,
        resume_download=True,
    ).eval()
    model.generation_config.max_new_tokens = 2048  # For chat.

    return model, tokenizer


def _chat_stream(model, tokenizer, query, history):
    conversation = []
    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})
    input_text = tokenizer.apply_chat_template(
        conversation,
        add_generation_prompt=True,
        tokenize=False,
    )
    inputs = tokenizer([input_text], return_tensors="pt").to(model.device)
    streamer = TextIteratorStreamer(
        tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True
    )
    generation_kwargs = {
        **inputs,
        "streamer": streamer,
    }
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    for new_text in streamer:
        yield new_text


def _gc():
    import gc

    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()


def _launch_demo(args, model, tokenizer):
    def predict(_query, _chatbot, _task_history):
        print(f"User: {_query}")
        _chatbot.append((_query, ""))
        full_response = ""
        response = ""
        for new_text in _chat_stream(model, tokenizer, _query, history=_task_history):
            response += new_text
            _chatbot[-1] = (_query, response)

            yield _chatbot
            full_response = response

        print(f"History: {_task_history}")
        _task_history.append((_query, full_response))
        print(f"Qwen: {full_response}")

    def regenerate(_chatbot, _task_history):
        if not _task_history:
            yield _chatbot
            return
        item = _task_history.pop(-1)
        _chatbot.pop(-1)
        yield from predict(item[0], _chatbot, _task_history)

    def reset_user_input():
        return gr.update(value="")

    def reset_state(_chatbot, _task_history):
        _task_history.clear()
        _chatbot.clear()
        _gc()
        return _chatbot

    with gr.Blocks() as demo:
        gr.Markdown("""\
<p align="center"><img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/assets/logo/qwen2.5_logo.png" style="height: 120px"/><p>""")
        gr.Markdown(
            """\
<center><font size=3>This WebUI is based on Qwen2.5-Instruct, developed by Alibaba Cloud. \
(本WebUI基于Qwen2.5-Instruct打造，实现聊天机器人功能。)</center>"""
        )
        gr.Markdown("""\
<center><font size=4>
Qwen2.5-7B-Instruct <a href="https://modelscope.cn/models/qwen/Qwen2.5-7B-Instruct/summary">🤖 </a> | 
<a href="https://huggingface.co/Qwen/Qwen2.5-7B-Instruct">🤗</a>&nbsp ｜ 
Qwen2.5-32B-Instruct <a href="https://modelscope.cn/models/qwen/Qwen2.5-32B-Instruct/summary">🤖 </a> | 
<a href="https://huggingface.co/Qwen/Qwen2.5-32B-Instruct">🤗</a>&nbsp ｜ 
Qwen2.5-72B-Instruct <a href="https://modelscope.cn/models/qwen/Qwen2.5-72B-Instruct/summary">🤖 </a> | 
<a href="https://huggingface.co/Qwen/Qwen2.5-72B-Instruct">🤗</a>&nbsp ｜ 
&nbsp<a href="https://github.com/QwenLM/Qwen2.5">Github</a></center>""")

        chatbot = gr.Chatbot(label="Qwen", elem_classes="control-height")
        query = gr.Textbox(lines=2, label="Input")
        task_history = gr.State([])

        with gr.Row():
            empty_btn = gr.Button("🧹 Clear History (清除历史)")
            submit_btn = gr.Button("🚀 Submit (发送)")
            regen_btn = gr.Button("🤔️ Regenerate (重试)")

        submit_btn.click(
            predict, [query, chatbot, task_history], [chatbot], show_progress=True
        )
        submit_btn.click(reset_user_input, [], [query])
        empty_btn.click(
            reset_state, [chatbot, task_history], outputs=[chatbot], show_progress=True
        )
        regen_btn.click(
            regenerate, [chatbot, task_history], [chatbot], show_progress=True
        )

        gr.Markdown("""\
<font size=2>Note: This demo is governed by the original license of Qwen2.5. \
We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, \
including hate speech, violence, pornography, deception, etc. \
(注：本演示受Qwen2.5的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，\
包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)""")

    demo.queue().launch(
        share=args.share,
        inbrowser=args.inbrowser,
        server_port=args.server_port,
        server_name=args.server_name,
    )


def main():
    args = _get_args()

    model, tokenizer = _load_model_tokenizer(args)

    _launch_demo(args, model, tokenizer)


if __name__ == "__main__":
    main()

三、运行CLI命令行demo

python cli_demo.py

输入问题开始对话，输入 :q 即可退出

四、运行 Web demo

1.开启服务demo

python web_demo.py

2.下载Autodl平台SSH工具

1.返回Autodl主界面，选择自定义服务

2.下载SSH Tool到本机电脑，并解压

3.通过SSH连接远程服务端口

打开解压的文件，点击AutoDL.exe并运行，弹出以下窗口

其中服务器的远程指令和密码可以直接复制

点击“开始代理”后卡住是正常的，耐心等待半分钟即可，成功后点击访问即可开启对话

---

恭喜你成功调用了自己部署的大模型！！！

上一章 一文彻底搞定从0到1手把手教你部署Qwen3大模型