【新手指南】pyqt可视化远程部署deepseek7B蒸馏版模型
本地效果：（如果想做这个的本科毕设，建议美化界面。）
总结：MobaXterm远程连接autodl服务器，在MobaXterm上利用X11转发使pyqt可视化页面在自己的电脑上展现出来。
1. 官网下载MobaXterm
MobaXterm free Xserver and tabbed SSH client for Windows，我认为这个比pycharm专业版连接autodl服务器更加好用。pycharm需要考虑同步，MobaXterm不需要点那么多同步设置。在MobaXterm上操作也比autodl上的jupyterlab好操作。
2. 在autodl上租服务器
一般一个小时1.6-2r。这里注意显存大小能否支撑起模型的参数量（关于这个具体是怎么判断的，我还不会，会了再补充）我租的是4090D，建议起个早租卡，现在是晚上八点，我刚才创建新的实例租卡租不了，但是早上八点我看有富余。
镜像可以选择社区镜像，我用的是V3版本，它自带了1.5B的模型。deepseek-ai/DeepSeek-R1/DeepSeek-R1: 【一键聊天，多维研究】 DeepSeek-R1，目前位居全球大模型竞技榜前三，性能对标OpenAIo1正式版。深度求索（DeepSeek）公司在2025年1月20日发布的最新模型！ - CG
3. MobaXterm连接autodl服务器
【MobaXterm】登录与连接服务器教程_mobaxterm怎么连接服务器-CSDN博客
4. 我下载了7B的模型，注意下面下载到autodl-tmp目录中。
conda activate torch_env
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --local-dir /root/autodl-tmp/DeepSeek-R1-Distill-Qwen-7B --resume-download7B模型运行相比1.5那个应该只改了模型路径，运行后可以在终端与模型对话，输入exit退出：
文件名：deepseek_multichat-7B.py
终端运行输入：python deepseek_multichat-7B.py
import os
import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import unicodedata
from typing import List
@torch.inference_mode()
def generate(
model: AutoModelForCausalLM,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
max_new_tokens: int,
temperature: float = 1.0
) -> List[int]:
"""
Generate response from the model with attention_mask provided.
"""
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask, # 提供显式 attention mask
max_new_tokens=max_new_tokens,
temperature=temperature,
eos_token_id=model.config.eos_token_id,
pad_token_id=model.config.eos_token_id,
do_sample=True,
top_k=50,
top_p=0.95,
)
return outputs[0].tolist()
def clean_input(user_input):
"""
清理用户输入，去除不可见字符和多余的空格。
"""
user_input = "".join(c for c in user_input if not unicodedata.category(c).startswith("C")) # 移除控制字符
return user_input.strip() # 去除首尾空格
def clean_message_content(content):
"""
清理消息内容，去除首尾空格并过滤非法输入
"""
if not content or not isinstance(content, str):
return ""
return content.strip() # 去除首尾空格
def build_prompt(messages, max_history=3):
"""
Build prompt for the model, limiting the history to the most recent messages.
"""
template = "The following is a conversation with an AI assistant. The assistant is helpful, knowledgeable, and polite:\n"
for msg in messages[-max_history:]:
content = clean_message_content(msg["content"])
if not content: # 跳过空内容
continue
template += f"{msg['role'].capitalize()}: {content}\n"
template += "Assistant: "
return template.strip() # 确保返回值是字符串
if __name__ == "__main__":
print("Initializing DeepSeek-R1 Service...")
# Configuration
ckpt_path = "/root/autodl-tmp/DeepSeek-R1-Distill-Qwen-7B" # 模型所在的目录
config_path = "/root/autodl-tmp/DeepSeek-R1-Distill-Qwen-7B/config.json" # 配置文件路径
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
model = AutoModelForCausalLM.from_pretrained(
ckpt_path,
torch_dtype=torch.bfloat16,
).cuda()
# Interactive session
messages = [] # To maintain context
while True:
user_input = input("You: ").strip() # 去除首尾空格
user_input = clean_input(user_input) # 清理不可见字符
if not user_input or len(user_input.strip()) == 0: # 检查无效输入
print("Invalid input. Please type something meaningful!")
continue
if user_input.lower() in ["exit", "quit"]:
print("Exiting conversation. Goodbye!")
break
# Append user input to context
messages.append({"role": "user", "content": user_input})
# Limit conversation history
messages = messages[-10:] # 只保留最近 10 条对话
# Build prompt and tokenize
prompt = build_prompt(messages)
if not isinstance(prompt, str) or len(prompt.strip()) == 0: # 确保 prompt 非空
print("Error: Prompt is empty or invalid. Skipping this turn.")
continue
tokenized_prompt = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
input_ids = tokenized_prompt["input_ids"].to("cuda")
attention_mask = tokenized_prompt["attention_mask"].to("cuda")
# Generate response
max_new_tokens = 500 #150
temperature = 0.7
completion_tokens = generate(model, input_ids, attention_mask, max_new_tokens, temperature)
completion = tokenizer.decode(
completion_tokens[len(input_ids[0]):], # 从输入长度截取生成部分
skip_special_tokens=True
).split("User:")[0].strip()
print(f"Assistant: {completion}")
# Append assistant response to context
messages.append({"role": "assistant", "content": completion})
5. 接下来是最让我头疼的pyqt图形化页面显示。
autodl上是不能直接运行出图形化页面的。可以解决有两种方式：第一种方法：模型利用flask打包成api接口，然后在自己电脑上访问这个接口，在自己电脑上显示出来图形化页面；第二种方法：利用X11转发功能，在autodl上运行图形化页面程序，然后在自己电脑上显示。我用的第二种方法，第一种方法是问了师兄，这个还没尝试，之后尝试了再记录。
一开始用Xlaunch不行，MobaXterm就不需要那么多配置。
如果X11 Forward 显示红叉 可以参考：MobaXterm连接服务器，通过x11 Forwarding实现图形可视化（记录个人学习过程）_mobaxterm x11-CSDN博客
最重要的应该是那个X11UseLocalhost no，我用的pyqt，不需要localhost，也设置了no
pyqt页面代码：
import sys
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
import os
import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import unicodedata
from typing import List
# 原模型相关代码保持不变
# [此处插入原文件中的模型加载和生成相关函数，保持原样]
@torch.inference_mode()
def generate(
model: AutoModelForCausalLM,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
max_new_tokens: int,
temperature: float = 1.0
) -> List[int]:
"""
Generate response from the model with attention_mask provided.
"""
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask, # 提供显式 attention mask
max_new_tokens=max_new_tokens,
temperature=temperature,
eos_token_id=model.config.eos_token_id,
pad_token_id=model.config.eos_token_id,
do_sample=True,
top_k=50,
top_p=0.95,
)
return outputs[0].tolist()
def clean_input(user_input):
"""
清理用户输入，去除不可见字符和多余的空格。
"""
user_input = "".join(c for c in user_input if not unicodedata.category(c).startswith("C")) # 移除控制字符
return user_input.strip() # 去除首尾空格
def clean_message_content(content):
"""
清理消息内容，去除首尾空格并过滤非法输入
"""
if not content or not isinstance(content, str):
return ""
return content.strip() # 去除首尾空格
def build_prompt(messages, max_history=3):
"""
Build prompt for the model, limiting the history to the most recent messages.
"""
template = "The following is a conversation with an AI assistant. The assistant is helpful, knowledgeable, and polite:\n"
for msg in messages[-max_history:]:
content = clean_message_content(msg["content"])
if not content: # 跳过空内容
continue
template += f"{msg['role'].capitalize()}: {content}\n"
template += "Assistant: "
return template.strip() # 确保返回值是字符串
class ChatWindow(QWidget):
def __init__(self, model, tokenizer):
super().__init__()
self.model = model
self.tokenizer = tokenizer
self.messages = []
self.init_ui()
def init_ui(self):
self.setWindowTitle('DeepSeek Chat')
self.setGeometry(300, 300, 800, 600)
# 创建界面组件
self.history_area = QTextEdit()
self.history_area.setReadOnly(True)
self.input_area = QTextEdit()
self.input_area.setMaximumHeight(100)
self.send_btn = QPushButton('发送')
self.clear_btn = QPushButton('清空历史')
# 布局设置
vbox = QVBoxLayout()
vbox.addWidget(self.history_area)
hbox = QHBoxLayout()
hbox.addWidget(self.input_area)
hbox.addWidget(self.send_btn)
hbox.addWidget(self.clear_btn)
vbox.addLayout(hbox)
self.setLayout(vbox)
# 信号连接
self.send_btn.clicked.connect(self.on_send)
self.clear_btn.clicked.connect(self.on_clear)
self.input_area.installEventFilter(self)
def eventFilter(self, obj, event):
if obj is self.input_area and event.type() == QEvent.KeyPress:
if event.key() == Qt.Key_Return and event.modifiers() & Qt.ControlModifier:
self.on_send()
return True
return super().eventFilter(obj, event)
def update_history(self, role, content):
self.history_area.append(f"<b>{role.capitalize()}:</b> {content}<br>")
self.history_area.verticalScrollBar().setValue(
self.history_area.verticalScrollBar().maximum()
)
def on_clear(self):
self.messages = []
self.history_area.clear()
def on_send(self):
user_input = self.input_area.toPlainText().strip()
if not user_input:
return
# 清理输入
user_input = clean_input(user_input)
self.input_area.clear()
self.update_history("user", user_input)
# 添加到消息历史
self.messages.append({"role": "user", "content": user_input})
self.messages = self.messages[-10:]
# 在后台线程生成回复
self.worker = Worker(self.model, self.tokenizer, self.messages)
self.worker.finished.connect(self.handle_response)
self.worker.start()
def handle_response(self, completion):
self.messages.append({"role": "assistant", "content": completion})
self.update_history("assistant", completion)
class Worker(QThread):
finished = pyqtSignal(str)
def __init__(self, model, tokenizer, messages):
super().__init__()
self.model = model
self.tokenizer = tokenizer
self.messages = messages
def run(self):
prompt = build_prompt(self.messages)
tokenized_prompt = self.tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
input_ids = tokenized_prompt["input_ids"].to("cuda")
attention_mask = tokenized_prompt["attention_mask"].to("cuda")
max_new_tokens = 500
temperature = 0.7
completion_tokens = generate(self.model, input_ids, attention_mask, max_new_tokens, temperature)
completion = self.tokenizer.decode(
completion_tokens[len(input_ids[0]):],
skip_special_tokens=True
).split("User:")[0].strip()
self.finished.emit(completion)
if __name__ == "__main__":
# 初始化模型（原代码部分）
print("Initializing DeepSeek-R1 Service...")
ckpt_path = "/root/autodl-tmp/DeepSeek-R1-Distill-Qwen-7B"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
model = AutoModelForCausalLM.from_pretrained(
ckpt_path,
torch_dtype=torch.bfloat16,
).cuda()
# 启动GUI
app = QApplication(sys.argv)
window = ChatWindow(model, tokenizer)
window.show()
sys.exit(app.exec_())