二、进行网络攻击等

2.1环境方面准备

 创建项目，选择B1.medium规格服务器

选择CUDA12.1 PyTorch2.1.1的镜像

在模型中搜索Qwen2-0.5B-Instruct

点击启动后，即可进入环境。

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamerfrom threading import Thread device = "cuda" model_name = "/gemini/pretrain/Qwen2-0.5B-Instruct"model = AutoModelForCausalLM.from_pretrained(model_name).to(device)tokenizer = AutoTokenizer.from_pretrained(model_name)streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)messages = [    {"role": "system", "content": "You are a helpful assistant."},]/root/miniconda3/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html  from .autonotebook import tqdm as notebook_tqdmSpecial tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.In [2]:def stream_generate(prompt, model, tokenizer, device):    messages.append({"role": "user", "content": prompt})    text = tokenizer.apply_chat_template(        messages,        tokenize=False,        add_generation_prompt=True    )    model_inputs = tokenizer([text], return_tensors="pt").to(device)    generate_params = dict(        inputs=model_inputs.input_ids,        max_new_tokens=512,        do_sample=True,        top_k=50,        temperature=0.7,        pad_token_id=tokenizer.eos_token_id,        streamer=streamer    )        thread = Thread(target=model.generate, kwargs=generate_params)    thread.start()     generated_text = ""    for new_text in streamer:        generated_text += new_text        print(new_text, end='', flush=True)    print()    # generated_text    messages.append({"role": "user", "content": generated_text})In [3]:# 多轮对话while True:    user_input = input("User: ")    if user_input.lower() == 'exit':        print("Exiting...")        break        # 生成回复并流式输出    print("Assistant: ", end="")    stream_generate(user_input, model, tokenizer, device)

该代码的目的是使用一个预训练的因果语言模型（在这里是 /gemini/pretrain/Qwen2-0.5B-Instruct）来生成对用户输入的响应。BERT等。内容推荐等。

2.6使用streamlit框架构建即时问答系统

import streamlit as stfrom threading import Threadimport jsonimport timefrom transformers import AutoTokenizerfrom transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamerimport torch st.title('Qwen2-0.5B-Instruct') device = "cuda"model_name = "/gemini/pretrain/Qwen2-0.5B-Instruct" @st.cache_resourcedef get_model():      model = AutoModelForCausalLM.from_pretrained(model_name).to(device)    tokenizer = AutoTokenizer.from_pretrained(model_name)    return model, tokenizer model, tokenizer = get_model()streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) def generate_response(input_text):     prompt = f'''    你是一个智能问答助手，这是你需要回答的问题：    <question>    {input_text}    </question>    '''     print(prompt)    # 构建消息列表    messages = [{"role": "user", "content": prompt}]     text = tokenizer.apply_chat_template(        messages,        tokenize=False,        add_generation_prompt=True    )    model_inputs = tokenizer([text], return_tensors="pt").to(device)    generate_params = dict(        inputs=model_inputs.input_ids,        max_new_tokens=512,        do_sample=True,        top_k=50,        temperature=0.7,        pad_token_id=tokenizer.eos_token_id,        streamer=streamer    )        thread = Thread(target=model.generate, kwargs=generate_params)    thread.start()     return streamer with st.form('my_form'):    text = st.text_area('Enter text:', 'What are the three key pieces of advice for learning how to code?')    submitted = st.form_submit_button('Submit')    if submitted:        message_placeholder = st.empty()        # 调用模型生成响应        streamer = generate_response(text)        response = ""        for text in streamer:            response += text            message_placeholder.info(response + "▌")            message_placeholder.info(response)

Streamlit 是一个开源的 Python 库，用于快速创建和共享数据应用。

transformers 库由 Hugging Face 提供，它包含了大量预训练的模型，这些模型可以用于各种自然语言处理任务，如文本生成、