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<!doctype html>
<html>
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width" />
<title>My static Space</title>
<link rel="stylesheet" href="style.css" />
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/feather-icons/dist/feather.min.js"></script>
</head>
<body>
<div class="max-w-md mx-auto bg-white rounded-xl shadow-md overflow-hidden md:max-w-2xl m-4">
<div class="p-8">
<h1 class="text-2xl font-bold text-gray-800 mb-4">你好！欢迎来到静态空间</h1>
<p class="text-gray-600 mb-4">你可以直接编辑 <i class="font-mono">index.html</i> 文件来修改这个应用。</p>
<div class="flex items-center mb-4">
<i data-feather="book" class="mr-2 text-blue-500"></i>
<a href="https://huggingface.co/docs/hub/spaces" target="_blank" class="text-blue-500 hover:underline">空间文档</a>
</div>
<div class="flex items-center mb-4">
<i data-feather="globe" class="mr-2 text-green-500"></i>
<a href="about.html" class="text-green-500 hover:underline">关于我们</a>
</div>
<div class="flex items-center mb-4">
<i data-feather="mail" class="mr-2 text-purple-500"></i>
<a href="contact.html" class="text-purple-500 hover:underline">联系我们</a>
</div>
<div class="flex items-center mb-4">
<i data-feather="heart" class="mr-2 text-red-500"></i>
<a href="services.html" class="text-red-500 hover:underline">我们的服务</a>
</div>
<div class="flex items-center">
<i data-feather="user" class="mr-2 text-pink-500"></i>
<a href="beauty.html" class="text-pink-500 hover:underline">美女展示</a>
</div>
</div>
</div>
<script>
feather.replace();
</script>
<div class="max-w-md mx-auto bg-white rounded-xl shadow-md overflow-hidden md:max-w-2xl m-4">
<div class="p-8">
<h2 class="text-xl font-bold text-gray-800 mb-4">功能列表</h2>
<ul class="list-disc pl-5 text-gray-600">
<li class="mb-2">响应式设计</li>
<li class="mb-2">多语言支持</li>
<li class="mb-2">现代化UI</li>
</ul>
</div>
</div>
</body>
</html>
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-3.2-instruct-78b")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCaus |