轻松搭建自己的ChatGPT聊天机器人，让AI陪你聊天！

sockstack / 233 / 2023-11-19 12:00:31

<p><span style="color: red; font-size: 18px">ChatGPT 可用网址，仅供交流学习使用，如对您有所帮助，请收藏并推荐给需要的朋友。</span><br><a href="https://ckai.xyz/?sockstack&section=detail" target="__blank">https://ckai.xyz</a><br><br></p> <article class="baidu_pl"><div id="article_content" class="article_content clearfix">
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<svg xmlns="http://www.w3.org/2000/svg" style="display: none;"><path stroke-linecap="round" d="M5,0 0,2.5 5,5z" id="raphael-marker-block" style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0);"></path></svg><p>随着人工智能技术的发展，聊天机器人已经成为了我们生活中的一部分。无论是在客服机器人上还是智能助手上，聊天机器人都能够给我们带来真正的便利和快乐。现在，你也可以轻松搭建自己的ChatGPT聊天机器人，和它天马行空地聊天！</p> 
<h2>
<a id="_2"></a>第一步：准备所需材料</h2> 
<p>首先，你需要一些基本的技术知识和一些必备的软件工具，包括：</p> 
<ol>
<li> <p>Python基本语法：ChatGPT是使用Python开发的，因此你需要先掌握一些基本的Python语法。</p> </li>
<li> <p>PyTorch：ChatGPT是使用PyTorch构建的，因此在搭建ChatGPT之前，你需要先了解PyTorch的基本使用方法。</p> </li>
<li> <p>Transformers库：这是一个用于自然语言处理的Python库，可以帮助我们轻松地搭建和训练ChatGPT模型。</p> </li>
</ol> 
<h2>
<a id="ChatGPT_12"></a>第二步：搭建ChatGPT模型</h2> 
<p>现在你已经准备好了所有必要的软件工具，那么就让我们开始搭建ChatGPT模型吧！</p> 
<p>以下是一个简单的示例代码，可以使用Transformers和PyTorch搭建ChatGPT模型：</p> 
<pre><code class="prism language-python"><span class="token keyword">import</span> torch
<span class="token keyword">from</span> transformers <span class="token keyword">import</span> GPT2Tokenizer<span class="token punctuation">,</span> GPT2LMHeadModel<span class="token comment"># 加载预训练模型和分词器</span>
tokenizer <span class="token operator">=</span> GPT2Tokenizer<span class="token punctuation">.</span>from_pretrained<span class="token punctuation">(</span><span class="token string">'gpt2'</span><span class="token punctuation">)</span>
model <span class="token operator">=</span> GPT2LMHeadModel<span class="token punctuation">.</span>from_pretrained<span class="token punctuation">(</span><span class="token string">'gpt2'</span><span class="token punctuation">)</span><span class="token comment"># 输入文本</span>
text <span class="token operator">=</span> <span class="token string">'Hello, how are you?'</span><span class="token comment"># 将文本编码为ID</span>
input_ids <span class="token operator">=</span> tokenizer<span class="token punctuation">.</span>encode<span class="token punctuation">(</span>text<span class="token punctuation">,</span> return_tensors<span class="token operator">=</span><span class="token string">'pt'</span><span class="token punctuation">)</span><span class="token comment"># 生成响应</span>
output <span class="token operator">=</span> model<span class="token punctuation">.</span>generate<span class="token punctuation">(</span>input_ids<span class="token punctuation">,</span> max_length<span class="token operator">=</span><span class="token number">1000</span><span class="token punctuation">)</span><span class="token comment"># 将响应解码为文本</span>
output_text <span class="token operator">=</span> tokenizer<span class="token punctuation">.</span>decode<span class="token punctuation">(</span>output<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span> skip_special_tokens<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>output_text<span class="token punctuation">)</span>
</code></pre> 
<p>这段代码使用了GPT2模型和分词器，从输入文本中生成了一个1000个字符的响应。</p> 
<h2>
<a id="ChatGPT_42"></a>第三步：训练ChatGPT模型</h2> 
<p>如果你想让你的ChatGPT模型可以更加智能和对话更加流畅，那么你需要利用机器学习的方法对模型进行训练。</p> 
<p>以下是一个简单的示例代码，可以使用Transformers和PyTorch训练ChatGPT模型：</p> 
<pre><code class="prism language-python"><span class="token keyword">import</span> torch
<span class="token keyword">from</span> transformers <span class="token keyword">import</span> GPT2Tokenizer<span class="token punctuation">,</span> GPT2LMHeadModel<span class="token punctuation">,</span> TextDataset<span class="token punctuation">,</span> DataCollatorForLanguageModeling<span class="token punctuation">,</span> Trainer<span class="token punctuation">,</span> TrainingArguments<span class="token comment"># 加载预训练模型和分词器</span>
tokenizer <span class="token operator">=</span> GPT2Tokenizer<span class="token punctuation">.</span>from_pretrained<span class="token punctuation">(</span><span class="token string">'gpt2'</span><span class="token punctuation">)</span>
model <span class="token operator">=</span> GPT2LMHeadModel<span class="token punctuation">.</span>from_pretrained<span class="token punctuation">(</span><span class="token string">'gpt2'</span><span class="token punctuation">)</span><span class="token comment"># 加载数据集</span>
dataset <span class="token operator">=</span> TextDataset<span class="token punctuation">(</span>tokenizer<span class="token operator">=</span>tokenizer<span class="token punctuation">,</span> file_path<span class="token operator">=</span><span class="token string">'data.txt'</span><span class="token punctuation">,</span> block_size<span class="token operator">=</span><span class="token number">128</span><span class="token punctuation">)</span><span class="token comment"># 设置训练参数</span>
training_args <span class="token operator">=</span> TrainingArguments<span class="token punctuation">(</span>output_dir<span class="token operator">=</span><span class="token string">'./results'</span><span class="token punctuation">,</span> <span class="token comment"># output directory</span>overwrite_output_dir<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">,</span> <span class="token comment"># overwrite the content of the output directory</span>num_train_epochs<span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token comment"># number of training epochs</span>per_device_train_batch_size<span class="token operator">=</span><span class="token number">32</span><span class="token punctuation">,</span> <span class="token comment"># batch size for training</span>save_steps<span class="token operator">=</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token comment"># save checkpoint every 1000 steps</span>save_total_limit<span class="token operator">=</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token comment"># only keep last 2 checkpoints</span>warmup_steps<span class="token operator">=</span><span class="token number">500</span><span class="token punctuation">,</span> <span class="token comment"># number of warmup steps for learning rate scheduler</span>weight_decay<span class="token operator">=</span><span class="token number">0.01</span><span class="token punctuation">,</span> <span class="token comment"># strength of weight decay</span>logging_dir<span class="token operator">=</span><span class="token string">'./logs'</span><span class="token punctuation">,</span> <span class="token comment"># directory for storing logs</span>logging_steps<span class="token operator">=</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token comment"># log every 1000 steps</span><span class="token punctuation">)</span><span class="token comment"># 设置DataCollator</span>
data_collator <span class="token operator">=</span> DataCollatorForLanguageModeling<span class="token punctuation">(</span>tokenizer<span class="token operator">=</span>tokenizer<span class="token punctuation">,</span> mlm<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">,</span>
<span class="token punctuation">)</span><span class="token comment"># 构建Trainer</span>
trainer <span class="token operator">=</span> Trainer<span class="token punctuation">(</span>model<span class="token operator">=</span>model<span class="token punctuation">,</span>args<span class="token operator">=</span>training_args<span class="token punctuation">,</span>data_collator<span class="token operator">=</span>data_collator<span class="token punctuation">,</span>train_dataset<span class="token operator">=</span>dataset<span class="token punctuation">,</span>
<span class="token punctuation">)</span><span class="token comment"># 开始训练</span>
trainer<span class="token punctuation">.</span>train<span class="token punctuation">(</span><span class="token punctuation">)</span>
</code></pre> 
<p>这段代码使用了TextDataset和DataCollatorForLanguageModeling来读取和处理数据集，使用GPT2模型和分词器训练ChatGPT模型，并将结果保存在results目录中。</p> 
<h2>
<a id="ChatGPT_92"></a>第四步：让ChatGPT机器人和你聊天</h2> 
<p>现在你已经成功搭建和训练了自己的ChatGPT机器人了！那么让我们来看看如何和它进行聊天。</p> 
<p>以下是一个简单的示例代码，可以使用训练好的ChatGPT机器人进行聊天：</p> 
<pre><code class="prism language-python"><span class="token keyword">import</span> torch
<span class="token keyword">from</span> transformers <span class="token keyword">import</span> GPT2Tokenizer<span class="token punctuation">,</span> GPT2LMHeadModel<span class="token comment"># 加载训练好的模型和分词器</span>
tokenizer <span class="token operator">=</span> GPT2Tokenizer<span class="token punctuation">.</span>from_pretrained<span class="token punctuation">(</span><span class="token string">'results'</span><span class="token punctuation">)</span>
model <span class="token operator">=</span> GPT2LMHeadModel<span class="token punctuation">.</span>from_pretrained<span class="token punctuation">(</span><span class="token string">'results'</span><span class="token punctuation">)</span><span class="token comment"># 开始聊天</span>
<span class="token keyword">while</span> <span class="token boolean">True</span><span class="token punctuation">:</span><span class="token comment"># 获取用户输入</span>user_input <span class="token operator">=</span> <span class="token builtin">input</span><span class="token punctuation">(</span><span class="token string">'You: '</span><span class="token punctuation">)</span><span class="token comment"># 将用户输入编码为ID</span>input_ids <span class="token operator">=</span> tokenizer<span class="token punctuation">.</span>encode<span class="token punctuation">(</span>user_input<span class="token punctuation">,</span> return_tensors<span class="token operator">=</span><span class="token string">'pt'</span><span class="token punctuation">)</span><span class="token comment"># 生成响应</span>output <span class="token operator">=</span> model<span class="token punctuation">.</span>generate<span class="token punctuation">(</span>input_ids<span class="token punctuation">,</span> max_length<span class="token operator">=</span><span class="token number">1000</span><span class="token punctuation">)</span><span class="token comment"># 将响应解码为文本</span>output_text <span class="token operator">=</span> tokenizer<span class="token punctuation">.</span>decode<span class="token punctuation">(</span>output<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span> skip_special_tokens<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span><span class="token comment"># 输出机器人的响应</span><span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'ChatGPT: '</span> <span class="token operator">+</span> output_text<span class="token punctuation">)</span>
</code></pre> 
<p>这段代码使用了训练好的模型和分词器，可以和ChatGPT机器人进行聊天交流！</p> 
<h2>
<a id="_126"></a>结语</h2> 
<p>通过这份资料，你已经学会了如何搭建、训练和使用ChatGPT聊天机器人了！希望这份资料能够帮助你打造出一款智能、有趣的聊天机器人，让你的生活充满更多色彩！</p>
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轻松搭建自己的ChatGPT聊天机器人，让AI陪你聊天！

作者

sockstack

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CC BY 4.0

发布于

2023-11-19

修改于

2024-12-25

上一篇：软件：常用 Linux 软件汇总，值得收藏下一篇：ChatGPT API接口使用+fine tune微调+prompt介绍

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