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class="container" id="main-content" tabindex="-1"><div class="post"><article class="post-block h-entry"><div class="post-meta p-author h-card visually-hidden"><img class="author-avatar u-photo" src="../favicon.png" alt="Cytrogen"><span class="p-name">Cytrogen</span><a class="u-url" href="https://cytrogen.icu">https://cytrogen.icu</a></div><a class="post-permalink u-url u-uid visually-hidden" href="https://cytrogen.icu/posts/40b3.html">永久链接</a><div class="p-summary visually-hidden"><p>GPT-3 和 GPT-3.5-Turbo API 的学习日志，仅作个人用途。</p></div><div class="visually-hidden"><a class="p-category" href="../categories/%E7%BC%96%E7%A8%8B%E7%AC%94%E8%AE%B0/">编程笔记</a><a class="p-category" href="../tags/Python/">Python</a><a class="p-category" href="../tags/AI/">AI</a></div><h1 class="post-title p-name">GPT-3 API 笔记</h1><div class="post-info"><time class="post-date dt-published" datetime="2023-03-09T19:02:26.000Z">3/9/2023</time><time class="dt-updated visually-hidden" datetime="2026-02-09T17:16:54.709Z"></time></div><div class="post-content e-content"><html><head></head><body><p>GPT-3 和 GPT-3.5-Turbo API 的学习日志，仅作个人用途。</p>
<span id="more"></span>
<h1 id="目录"><a class="markdownIt-Anchor" href="#目录"></a> 目录</h1>
<ul>
<li><a href="#%E7%9B%AE%E5%BD%95">目录</a></li>
<li><a href="#%E4%BA%8B%E5%85%88%E5%87%86%E5%A4%87">事先准备</a></li>
<li><a href="#%E5%88%9B%E5%BB%BA%E5%BE%AE%E8%B0%83%E6%A8%A1%E5%9E%8B">创建微调模型</a>
<ul>
<li><a href="#%E6%9F%A5%E7%9C%8B%E5%BE%AE%E8%B0%83%E5%B7%A5%E4%BD%9C">查看微调工作</a></li>
</ul>
</li>
<li><a href="#%E5%88%A0%E9%99%A4%E5%BE%AE%E8%B0%83%E6%A8%A1%E5%9E%8B">删除微调模型</a></li>
<li><a href="#%E4%BD%BF%E7%94%A8%E5%BE%AE%E8%B0%83%E6%A8%A1%E5%9E%8B">使用微调模型</a></li>
</ul>
<center> ------------------</center>
<h1 id="事先准备"><a class="markdownIt-Anchor" href="#事先准备"></a> 事先准备</h1>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">pip install openai</span><br><span class="line"></span><br><span class="line"><span class="comment"># 使用bash...</span></span><br><span class="line"><span class="built_in">export</span> OPENAI_API_KEY=<span class="string">"你的OpenAI API KEY"</span></span><br></pre></td></tr></tbody></table></figure>
<h1 id="创建微调模型"><a class="markdownIt-Anchor" href="#创建微调模型"></a> 创建微调模型</h1>
<div class="danger">
<p>注意，GPT-3.5-Turbo API 目前 <strong>不支持</strong> 微调！</p>
<p>该栏块说的 API 统一默认为 <strong>GPT-3</strong>！</p>
</div>
<p>微调 GPT-3 所需要的数据必须是 <code>JSONL</code> 文件，格式为：</p>
<figure class="highlight plaintext"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">{"prompt": "提白文本", "completion": "想要生成的文本"}</span><br></pre></td></tr></tbody></table></figure>
<p>假设已经有一个 <code>CSV</code>、<code>TSV</code>、<code>XLSX</code> 或 <code>JSON</code> 文件作为数据库，则可以使用以下命令行来重新格式化数据：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai tools fine_tunes.prepare_data -f 文件名</span><br></pre></td></tr></tbody></table></figure>
<p>有了 <code>JSONL</code> 文件后，开始微调：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai api fine_tunes.create -t JSONL文件名 -m 模型名</span><br></pre></td></tr></tbody></table></figure>
<blockquote>
<p>模型名分为：ada、babbage、curie、davinci。<br>
从左到右，速度越来越慢，能力越来越强。</p>
</blockquote>
<div class="danger">
<p>注意：</p>
<p>一定要仔细查看微调模型的价格表！</p>
<table>
<thead>
<tr>
<th>模型</th>
<th>训练</th>
<th>使用</th>
</tr>
</thead>
<tbody>
<tr>
<td>Ada</td>
<td>$0.0004 / 1K tokens</td>
<td>$0.0016 / 1K tokens</td>
</tr>
<tr>
<td>Babbage</td>
<td>$0.0006 / 1K tokens</td>
<td>$0.0024 / 1K tokens</td>
</tr>
<tr>
<td>Curie</td>
<td>$0.0030 / 1K tokens</td>
<td>$0.0120 / 1K tokens</td>
</tr>
<tr>
<td>Davinci</td>
<td>$0.0300 / 1K tokens</td>
<td>$0.1200 / 1K tokens</td>
</tr>
</tbody>
</table>
</div>
<p>有时事件流可能会被中断，使用以下命令行来恢复微调：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai api fine_tunes.follow -i 微调工作ID</span><br></pre></td></tr></tbody></table></figure>
<blockquote>
<p>事件流被中断后会自动生成一个命令供你复制粘贴运行。</p>
</blockquote>
<h2 id="查看微调工作"><a class="markdownIt-Anchor" href="#查看微调工作"></a> 查看微调工作</h2>
<p>列出所有被创建的微调工作：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai api fine_tunes.list</span><br></pre></td></tr></tbody></table></figure>
<p>检索一个微调工作的状态和其他信息：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai api fine_tunes.get -i 微调工作ID</span><br></pre></td></tr></tbody></table></figure>
<p>取消一个微调工作：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai api fine_tunes.cancel -i 微调工作ID</span><br></pre></td></tr></tbody></table></figure>
<h1 id="删除微调模型"><a class="markdownIt-Anchor" href="#删除微调模型"></a> 删除微调模型</h1>
<p>有光就有影子，有创建就有删除：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai api models.delete -i 微调模型</span><br></pre></td></tr></tbody></table></figure>
<p>Python 也可以删除一个微调模型：</p>
<figure class="highlight python"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> openai</span><br><span class="line">openai.Model.delete(微调模型)</span><br></pre></td></tr></tbody></table></figure>
<center>------------------</center>
<h1 id="使用微调模型"><a class="markdownIt-Anchor" href="#使用微调模型"></a> 使用微调模型</h1>
<p>你可以直接使用微调成功后，自动生成的命令行：</p>
<figure class="highlight bash"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">openai api completions.create -m 微调模型 -p 提白</span><br></pre></td></tr></tbody></table></figure>
<p>当然主要的还是我们的 Python，这是官网示范：</p>
<figure class="highlight python"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> openai</span><br><span class="line">openai.Completion.create(</span><br><span class="line"> model=FINE_TUNED_MODEL,</span><br><span class="line"> prompt=YOUR_PROMPT)</span><br></pre></td></tr></tbody></table></figure>
<p><code>openai.Completion.create()</code> 返回一个字典：</p>
<figure class="highlight python"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><span class="line">{</span><br><span class="line"> <span class="string">"choices"</span>: [</span><br><span class="line"> {</span><br><span class="line"> <span class="string">"finish_reason"</span>: <span class="string">"length"</span>,</span><br><span class="line"> <span class="string">"index"</span>: 数字,</span><br><span class="line"> <span class="string">"logprobs"</span>: null,</span><br><span class="line"> <span class="string">"text"</span>: <span class="string">"字符串"</span></span><br><span class="line"> }</span><br><span class="line"> ],</span><br><span class="line"> <span class="string">"created"</span>: 数字,</span><br><span class="line"> <span class="string">"id"</span>: <span class="string">"字符串"</span>,</span><br><span class="line"> <span class="string">"model"</span>: <span class="string">"微调模型"</span>,</span><br><span class="line"> <span class="string">"object"</span>: <span class="string">"text_completion"</span>,</span><br><span class="line"> <span class="string">"usage"</span>: {</span><br><span class="line"> <span class="string">"completion_tokens"</span>: 数字,</span><br><span class="line"> <span class="string">"prompt_tokens"</span>: 数字,</span><br><span class="line"> <span class="string">"total_tokens"</span>: 数字</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></tbody></table></figure>
<p>也就是说，只要拿到 <code>openai.Completion.create()['choices'][0]['text']</code>，就拿到了模型返回的字符串，我们打印这个字符串就好了。</p>
<p>稍作修改一番：</p>
<figure class="highlight python"><table><tbody><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> openai</span><br><span class="line"><span class="keyword">import</span> prompt_toolkit</span><br><span class="line"></span><br><span class="line"><span class="keyword">while</span> <span class="literal">True</span>:</span><br><span class="line"> user_input = prompt_toolkit.prompt(<span class="string">'> '</span>)</span><br><span class="line"> response = openai.Completion.create(</span><br><span class="line"> model=微调模型,</span><br><span class="line"> prompt=user_input</span><br><span class="line"> )</span><br><span class="line"> <span class="built_in">print</span>(response[<span class="string">'choices'</span>][<span class="number">0</span>][<span class="string">'text'</span>])</span><br></pre></td></tr></tbody></table></figure>
<p>一个简易的控制台机器人就写好了。</p>
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