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README.md +7 -5
index.html +669 -19

README.md CHANGED Viewed

@@ -1,10 +1,12 @@
 ---
-title: Deepseek
-emoji: 😻
-colorFrom: blue
-colorTo: purple
 sdk: static
 pinned: false
 ---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: deepseek
+emoji: 🐳
+colorFrom: pink
+colorTo: gray
 sdk: static
 pinned: false
+tags:
+  - deepsite
 ---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

index.html CHANGED Viewed

@@ -1,19 +1,669 @@
-<!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" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
-</html>

+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>DeepSeek V3 0324 部署指南</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+    <style>
+        .code-block {
+            background-color: #2d2d2d;
+            color: #f8f8f2;
+            padding: 1rem;
+            border-radius: 0.5rem;
+            font-family: 'Courier New', Courier, monospace;
+            overflow-x: auto;
+            margin: 1rem 0;
+            position: relative;
+        }
+        .copy-btn {
+            position: absolute;
+            right: 0.5rem;
+            top: 0.5rem;
+            background-color: #4a5568;
+            color: white;
+            border: none;
+            border-radius: 0.25rem;
+            padding: 0.25rem 0.5rem;
+            cursor: pointer;
+            font-size: 0.75rem;
+        }
+        .copy-btn:hover {
+            background-color: #2d3748;
+        }
+        .note {
+            background-color: #e3f2fd;
+            border-left: 4px solid #2196f3;
+            padding: 1rem;
+            margin: 1rem 0;
+            border-radius: 0 0.5rem 0.5rem 0;
+        }
+        .warning {
+            background-color: #fff8e1;
+            border-left: 4px solid #ffc107;
+            padding: 1rem;
+            margin: 1rem 0;
+            border-radius: 0 0.5rem 0.5rem 0;
+        }
+        .success {
+            background-color: #e8f5e9;
+            border-left: 4px solid #4caf50;
+            padding: 1rem;
+            margin: 1rem 0;
+            border-radius: 0 0.5rem 0.5rem 0;
+        }
+        .hardware-req {
+            background-color: #f5f5f5;
+            border-radius: 0.5rem;
+            padding: 1rem;
+            margin: 1rem 0;
+        }
+        .tab-content {
+            display: none;
+        }
+        .tab-content.active {
+            display: block;
+        }
+        .tab-button {
+            background-color: #f1f1f1;
+            border: none;
+            padding: 10px 16px;
+            cursor: pointer;
+            transition: 0.3s;
+            border-radius: 5px 5px 0 0;
+            margin-right: 5px;
+        }
+        .tab-button:hover {
+            background-color: #ddd;
+        }
+        .tab-button.active {
+            background-color: #4CAF50;
+            color: white;
+        }
+    </style>
+</head>
+<body class="bg-gray-50 text-gray-800">
+    <div class="container mx-auto px-4 py-8 max-w-5xl">
+        <header class="mb-8 text-center">
+            <h1 class="text-4xl font-bold text-green-700 mb-2">DeepSeek V3 0324 部署指南</h1>
+            <p class="text-xl text-gray-600">NVIDIA A6000 + Dell T7910 内网工作站</p>
+            <div class="flex justify-center mt-4">
+                <span class="bg-green-100 text-green-800 text-sm font-medium px-2.5 py-0.5 rounded mr-2">Ktransformers</span>
+                <span class="bg-blue-100 text-blue-800 text-sm font-medium px-2.5 py-0.5 rounded mr-2">Unsloth</span>
+                <span class="bg-purple-100 text-purple-800 text-sm font-medium px-2.5 py-0.5 rounded">GGUF量化模型</span>
+            </div>
+        </header>
+        <div class="bg-white rounded-lg shadow-md p-6 mb-8">
+            <h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2">
+                <i class="fas fa-info-circle text-blue-500 mr-2"></i>部署方案概述
+            </h2>
+            <p class="mb-4">本指南详细介绍了在NVIDIA A6000显卡的Dell T7910内网工作站上部署DeepSeek V3 0324大语言模型的完整流程，采用Ktransformers+Unsloth联合部署方案。</p>
+            <div class="grid grid-cols-1 md:grid-cols-2 gap-4 mb-6">
+                <div class="bg-gray-50 p-4 rounded-lg">
+                    <h3 class="font-medium text-lg mb-2 text-green-700"><i class="fas fa-laptop-code mr-2"></i>外网准备阶段</h3>
+                    <ul class="list-disc pl-5 space-y-1">
+                        <li>在可访问外网的Windows电脑上使用WSL</li>
+                        <li>完成所有依赖项的安装</li>
+                        <li>下载模型文件和配置</li>
+                    </ul>
+                </div>
+                <div class="bg-gray-50 p-4 rounded-lg">
+                    <h3 class="font-medium text-lg mb-2 text-blue-700"><i class="fas fa-server mr-2"></i>内网部署阶段</h3>
+                    <ul class="list-disc pl-5 space-y-1">
+                        <li>将完整环境复制到固态硬盘</li>
+                        <li>插入内网工作站启动</li>
+                        <li>验证模型运行</li>
+                    </ul>
+                </div>
+            </div>
+            <div class="note">
+                <i class="fas fa-lightbulb text-yellow-500 mr-2"></i>
+                <strong>提示：</strong> 本方案特别适合中国网络环境，尽可能使用国内下载源加速部署过程。
+            </div>
+        </div>
+        <div class="bg-white rounded-lg shadow-md p-6 mb-8">
+            <h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2">
+                <i class="fas fa-download text-purple-500 mr-2"></i>准备工作
+            </h2>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 系统要求</h3>
+            <div class="hardware-req">
+                <div class="grid grid-cols-1 md:grid-cols-2 gap-4">
+                    <div>
+                        <h4 class="font-medium mb-2"><i class="fas fa-desktop mr-2"></i>硬件配置</h4>
+                        <ul class="list-disc pl-5 space-y-1">
+                            <li>NVIDIA A6000显卡 (48GB显存)</li>
+                            <li>Dell Precision T7910工作站</li>
+                            <li>至少64GB系统内存</li>
+                            <li>高速固态硬盘(建议NVMe)</li>
+                        </ul>
+                    </div>
+                    <div>
+                        <h4 class="font-medium mb-2"><i class="fas fa-cog mr-2"></i>软件环境</h4>
+                        <ul class="list-disc pl-5 space-y-1">
+                            <li>Windows 10/11 with WSL2</li>
+                            <li>Ubuntu 20.04/22.04 LTS (WSL)</li>
+                            <li>Python 3.10+</li>
+                            <li>CUDA 11.8/12.1</li>
+                        </ul>
+                    </div>
+                </div>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 模型选择</h3>
+            <p class="mb-4">我们将使用ModelScope上的Unsloth提供的DeepSeek-V3-0324 GGUF量化模型，以下是可选模型及其硬件需求：</p>
+            <div class="overflow-x-auto">
+                <table class="min-w-full bg-white border border-gray-200">
+                    <thead class="bg-gray-100">
+                        <tr>
+                            <th class="py-2 px-4 border-b">模型名称</th>
+                            <th class="py-2 px-4 border-b">量化级别</th>
+                            <th class="py-2 px-4 border-b">显存需求</th>
+                            <th class="py-2 px-4 border-b">内存需求</th>
+                            <th class="py-2 px-4 border-b">适用场景</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <td class="py-2 px-4 border-b">deepseek-v3-0324-Q2_K.gguf</td>
+                            <td class="py-2 px-4 border-b">Q2_K (极低精度)</td>
+                            <td class="py-2 px-4 border-b">~12GB</td>
+                            <td class="py-2 px-4 border-b">32GB+</td>
+                            <td class="py-2 px-4 border-b">快速推理，低资源</td>
+                        </tr>
+                        <tr class="bg-gray-50">
+                            <td class="py-2 px-4 border-b">deepseek-v3-0324-Q4_K_M.gguf</td>
+                            <td class="py-2 px-4 border-b">Q4_K_M (中等精度)</td>
+                            <td class="py-2 px-4 border-b">~18GB</td>
+                            <td class="py-2 px-4 border-b">48GB+</td>
+                            <td class="py-2 px-4 border-b">平衡精度与速度</td>
+                        </tr>
+                        <tr>
+                            <td class="py-2 px-4 border-b">deepseek-v3-0324-Q5_K_M.gguf</td>
+                            <td class="py-2 px-4 border-b">Q5_K_M (较高精度)</td>
+                            <td class="py-2 px-4 border-b">~22GB</td>
+                            <td class="py-2 px-4 border-b">64GB+</td>
+                            <td class="py-2 px-4 border-b">高质量推理</td>
+                        </tr>
+                        <tr class="bg-gray-50">
+                            <td class="py-2 px-4 border-b">deepseek-v3-0324-Q6_K.gguf</td>
+                            <td class="py-2 px-4 border-b">Q6_K (高精度)</td>
+                            <td class="py-2 px-4 border-b">~26GB</td>
+                            <td class="py-2 px-4 border-b">64GB+</td>
+                            <td class="py-2 px-4 border-b">最高质量推理</td>
+                        </tr>
+                    </tbody>
+                </table>
+            </div>
+            <div class="note mt-4">
+                <i class="fas fa-lightbulb text-yellow-500 mr-2"></i>
+                <strong>建议：</strong> 对于NVIDIA A6000显卡(48GB显存)，推荐使用Q5_K_M或Q6_K量化级别的模型，以获得最佳性能与质量的平衡。
+            </div>
+        </div>
+        <div class="bg-white rounded-lg shadow-md p-6 mb-8">
+            <h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2">
+                <i class="fas fa-terminal text-green-500 mr-2"></i>外网环境部署 (WSL)
+            </h2>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 设置WSL环境</h3>
+            <p class="mb-4">在Windows电脑上启用WSL并安装Ubuntu:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 以管理员身份打开PowerShell
+wsl --install -d Ubuntu-22.04
+wsl --set-version Ubuntu-22.04 2
+wsl -d Ubuntu-22.04</code>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 配置Ubuntu环境</h3>
+            <p class="mb-4">在WSL的Ubuntu中执行以下命令:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 更新系统并安装基础工具
+sudo apt update && sudo apt upgrade -y
+sudo apt install -y build-essential cmake git wget python3-pip python3-venv
+# 配置国内源 (阿里云)
+sudo sed -i 's|http://archive.ubuntu.com|https://mirrors.aliyun.com|g' /etc/apt/sources.list
+sudo sed -i 's|http://security.ubuntu.com|https://mirrors.aliyun.com|g' /etc/apt/sources.list
+# 安装CUDA工具包 (使用国内源)
+wget https://developer.download.nvidia.cn/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
+sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
+wget https://developer.download.nvidia.cn/compute/cuda/12.1.1/local_installers/cuda-repo-wsl-ubuntu-12-1-local_12.1.1-1_amd64.deb
+sudo dpkg -i cuda-repo-wsl-ubuntu-12-1-local_12.1.1-1_amd64.deb
+sudo cp /var/cuda-repo-wsl-ubuntu-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/
+sudo apt-get update
+sudo apt-get -y install cuda
+# 验证CUDA安装
+nvidia-smi
+nvcc --version</code>
+            </div>
+            <div class="note mt-4">
+                <i class="fas fa-lightbulb text-yellow-500 mr-2"></i>
+                <strong>注意：</strong> 如果遇到网络问题，可以尝试使用清华源或中科大源替换阿里云源。
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">3. 创建Python虚拟环境</h3>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 创建虚拟环境
+python3 -m venv deepseek-env
+source deepseek-env/bin/activate
+# 配置pip国内源
+pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+pip install --upgrade pip
+# 安装基础依赖
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+pip install transformers accelerate sentencepiece ninja</code>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">4. 安装Ktransformers和Unsloth</h3>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 安装Ktransformers
+pip install ktransformers
+# 安装Unsloth (使用国内Git镜像)
+git clone https://gitee.com/mirrors/unsloth.git
+cd unsloth
+pip install -e .
+# 或者直接从PyPI安装 (可能较慢)
+# pip install unsloth</code>
+            </div>
+            <div class="warning mt-4">
+                <i class="fas fa-exclamation-triangle text-orange-500 mr-2"></i>
+                <strong>重要：</strong> 如果直接从PyPI安装速度过慢，建议使用Git镜像源克隆仓库后本地安装。
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">5. 下载DeepSeek V3 0324模型</h3>
+            <p class="mb-4">从ModelScope下载GGUF量化模型:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 创建模型目录
+mkdir -p ~/models/deepseek-v3-0324
+cd ~/models/deepseek-v3-0324
+# 使用国内镜像下载模型 (以Q5_K_M为例)
+wget https://modelscope.cn/api/v1/models/unsloth/DeepSeek-V3-0324-GGUF/repo?Revision=master&FilePath=deepseek-v3-0324-Q5_K_M.gguf -O deepseek-v3-0324-Q5_K_M.gguf
+# 可选: 下载其他量化级别的模型
+wget https://modelscope.cn/api/v1/models/unsloth/DeepSeek-V3-0324-GGUF/repo?Revision=master&FilePath=deepseek-v3-0324-Q4_K_M.gguf -O deepseek-v3-0324-Q4_K_M.gguf
+wget https://modelscope.cn/api/v1/models/unsloth/DeepSeek-V3-0324-GGUF/repo?Revision=master&FilePath=deepseek-v3-0324-Q6_K.gguf -O deepseek-v3-0324-Q6_K.gguf</code>
+            </div>
+            <div class="note mt-4">
+                <i class="fas fa-lightbulb text-yellow-500 mr-2"></i>
+                <strong>提示：</strong> 模型文件较大(10GB+)，请确保有足够的磁盘空间。下载完成后可以验证文件的MD5/SHA256校验和。
+            </div>
+        </div>
+        <div class="bg-white rounded-lg shadow-md p-6 mb-8">
+            <h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2">
+                <i class="fas fa-cogs text-blue-500 mr-2"></i>模型配置与测试
+            </h2>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 创建测试脚本</h3>
+            <p class="mb-4">创建一个Python脚本测试模型是否能正常运行:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 创建测试脚本
+cat &lt;&lt; 'EOF' &gt; test_deepseek.py
+from unsloth import FastLanguageModel
+import torch
+model_path = "/home/yourusername/models/deepseek-v3-0324/deepseek-v3-0324-Q5_K_M.gguf"
+model, tokenizer = FastLanguageModel.from_pretrained(model_path)
+# 配置模型参数
+FastLanguageModel.for_inference(model)
+model.config.use_cache = True
+model.config.max_seq_length = 4096  # 根据显存调整
+# 测试推理
+inputs = tokenizer("你好，DeepSeek V3！", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=64)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+EOF
+# 运行测试脚本
+python test_deepseek.py</code>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 优化配置参数</h3>
+            <p class="mb-4">根据NVIDIA A6000显卡的48GB显存，以下是推荐的配置参数:</p>
+            <div class="tabs mb-4">
+                <button class="tab-button active" onclick="openTab(event, 'q5-tab')">Q5_K_M 配置</button>
+                <button class="tab-button" onclick="openTab(event, 'q6-tab')">Q6_K 配置</button>
+                <button class="tab-button" onclick="openTab(event, 'q4-tab')">Q4_K_M 配置</button>
+            </div>
+            <div id="q5-tab" class="tab-content active">
+                <div class="code-block">
+                    <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                    <code># Q5_K_M 量化模型配置 (推荐)
+model_config = {
+    "model_path": "/path/to/deepseek-v3-0324-Q5_K_M.gguf",
+    "n_gpu_layers": 40,  # 使用尽可能多的GPU层
+    "n_ctx": 4096,       # 上下文长度
+    "n_batch": 512,      # 批处理大小
+    "n_threads": 12,     # CPU线程数(根据CPU核心数调整)
+    "max_new_tokens": 1024,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "repetition_penalty": 1.1,
+}</code>
+                </div>
+            </div>
+            <div id="q6-tab" class="tab-content">
+                <div class="code-block">
+                    <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                    <code># Q6_K 量化模型配置 (高质量)
+model_config = {
+    "model_path": "/path/to/deepseek-v3-0324-Q6_K.gguf",
+    "n_gpu_layers": 35,  # 减少GPU层数以适应更大模型
+    "n_ctx": 4096,
+    "n_batch": 384,      # 减小批处理大小
+    "n_threads": 12,
+    "max_new_tokens": 768,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "repetition_penalty": 1.1,
+}</code>
+                </div>
+            </div>
+            <div id="q4-tab" class="tab-content">
+                <div class="code-block">
+                    <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                    <code># Q4_K_M 量化模型配置 (高性能)
+model_config = {
+    "model_path": "/path/to/deepseek-v3-0324-Q4_K_M.gguf",
+    "n_gpu_layers": 45,  # 可以使用更多GPU层
+    "n_ctx": 4096,
+    "n_batch": 768,      # 增大批处理大小
+    "n_threads": 12,
+    "max_new_tokens": 2048,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "repetition_penalty": 1.1,
+}</code>
+                </div>
+            </div>
+            <div class="success mt-4">
+                <i class="fas fa-check-circle text-green-500 mr-2"></i>
+                <strong>验证：</strong> 如果测试脚本能正常运行并生成文本输出，说明模型已正确加载并可以使用。
+            </div>
+        </div>
+        <div class="bg-white rounded-lg shadow-md p-6 mb-8">
+            <h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2">
+                <i class="fas fa-exchange-alt text-purple-500 mr-2"></i>迁移到内网工作站
+            </h2>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 准备迁移内容</h3>
+            <p class="mb-4">将以下内容复制到固态硬盘:</p>
+            <ul class="list-disc pl-5 space-y-1 mb-4">
+                <li>完整的WSL Ubuntu系统 (导出为tar��件)</li>
+                <li>模型文件 (~/models/deepseek-v3-0324/)</li>
+                <li>Python虚拟环境 (~/deepseek-env/)</li>
+                <li>测试脚本和配置文件</li>
+            </ul>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 在WSL中导出Ubuntu系统
+wsl --export Ubuntu-22.04 ubuntu-22.04-deepseek.tar
+# 复制模型文件和虚拟环境
+cp -r ~/models /mnt/e/deepseek-deploy/
+cp -r ~/deepseek-env /mnt/e/deepseek-deploy/
+cp test_deepseek.py /mnt/e/deepseek-deploy/</code>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 在内网工作站上设置</h3>
+            <p class="mb-4">将固态硬盘插入内网工作站后执行以下步骤:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 1. 安装WSL (如果尚未安装)
+wsl --install
+# 2. 导入Ubuntu系统
+wsl --import Ubuntu-22.04-deepseek C:\WSL\Ubuntu-22.04-deepseek E:\ubuntu-22.04-deepseek.tar
+# 3. 设置默认用户 (替换yourusername)
+ubuntu2204.exe config --default-user yourusername
+# 4. 启动WSL
+wsl -d Ubuntu-22.04-deepseek</code>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">3. 验证内网环境</h3>
+            <p class="mb-4">在内网工作站的WSL中验证环境:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code># 激活虚拟环境
+source /mnt/e/deepseek-deploy/deepseek-env/bin/activate
+# 验证CUDA
+nvidia-smi
+nvcc --version
+# 运行测试脚本
+python /mnt/e/deepseek-deploy/test_deepseek.py</code>
+            </div>
+            <div class="warning mt-4">
+                <i class="fas fa-exclamation-triangle text-orange-500 mr-2"></i>
+                <strong>注意：</strong> 确保内网工作站已安装相同或兼容版本的NVIDIA驱动和CUDA工具包。
+            </div>
+        </div>
+        <div class="bg-white rounded-lg shadow-md p-6 mb-8">
+            <h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2">
+                <i class="fas fa-rocket text-red-500 mr-2"></i>高级配置与优化
+            </h2>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 使用Ktransformers加速</h3>
+            <p class="mb-4">结合Ktransformers可以进一步提高推理速度:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code>from ktransformers import AutoModelForCausalLM
+from unsloth import FastLanguageModel
+# 加载模型
+model_path = "/path/to/deepseek-v3-0324-Q5_K_M.gguf"
+model, tokenizer = FastLanguageModel.from_pretrained(model_path)
+# 转换为Ktransformers格式
+kmodel = AutoModelForCausalLM.from_pretrained(model, device_map="auto")
+# 配置生成参数
+generation_config = {
+    "max_new_tokens": 1024,
+    "do_sample": True,
+    "temperature": 0.7,
+    "top_p": 0.9,
+}
+# 推理示例
+inputs = tokenizer("中国的首都是", return_tensors="pt").to("cuda")
+outputs = kmodel.generate(**inputs, **generation_config)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))</code>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 批处理推理优化</h3>
+            <p class="mb-4">利用A6000的大显存进行批处理推理:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code>def batch_inference(queries, model, tokenizer, batch_size=4):
+    # 编码所有查询
+    inputs = tokenizer(queries, return_tensors="pt", padding=True, truncation=True).to("cuda")
+    # 分批处理
+    outputs = []
+    for i in range(0, len(queries), batch_size):
+        batch = {k: v[i:i+batch_size] for k, v in inputs.items()}
+        batch_outputs = model.generate(**batch, max_new_tokens=256)
+        outputs.extend(tokenizer.batch_decode(batch_outputs, skip_special_tokens=True))
+    return outputs
+# 示例使用
+queries = [
+    "解释人工智能的基本概念",
+    "写一首关于春天的诗",
+    "Python中如何实现快速排序?",
+    "中国的四大发明是什么?"
+]
+results = batch_inference(queries, kmodel, tokenizer)
+for q, r in zip(queries, results):
+    print(f"Q: {q}\nA: {r}\n{'='*50}")</code>
+            </div>
+            <h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">3. 性能监控脚本</h3>
+            <p class="mb-4">监控GPU使用情况和推理速度:</p>
+            <div class="code-block">
+                <button class="copy-btn" onclick="copyCode(this)">复制</button>
+                <code>import torch
+from pynvml import *
+def print_gpu_utilization():
+    nvmlInit()
+    handle = nvmlDeviceGetHandleByIndex(0)
+    info = nvmlDeviceGetMemoryInfo(handle)
+    print(f"GPU内存使用: {info.used//1024**2}MB / {info.total//1024**2}MB")
+    print(f"GPU利用率: {nvmlDeviceGetUtilizationRates(handle).gpu}%")
+# 在推理前后调用
+print_gpu_utilization()
+inputs = tokenizer("监控GPU使用情况", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print_gpu_utilization()</code>
+            </div>
+        </div>
+        <div class="bg-white rounded-lg shadow-md p-6 mb-8">
+            <h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2">
+                <i class="fas fa-question-circle text-indigo-500 mr-2"></i>常见问题解决
+            </h2>
+            <div class="space-y-4">
+                <div class="border-l-4 border-blue-500 pl-4 py-2">
+                    <h3 class="font-medium">1. CUDA out of memory 错误</h3>
+                    <p class="text-sm text-gray-600">解决方案：减少<code>n_gpu_layers</code>、降低<code>n_ctx</code>或<code>n_batch</code>，或使用更低量化的模型。</p>
+                </div>
+                <div class="border-l-4 border-blue-500 pl-4 py-2">
+                    <h3 class="font-medium">2. 模型加载缓慢</h3>
+                    <p class="text-sm text-gray-600">解决方案：确保模型文件在SSD上，增加<code>n_threads</code>参数使用更多CPU核心。</p>
+                </div>
+                <div class="border-l-4 border-blue-500 pl-4 py-2">
+                    <h3 class="font-medium">3. 推理速度不理想</h3>
+                    <p class="text-sm text-gray-600">解决方案：尝试使用Ktransformers，启用<code>use_cache</code>，并确保足够多的层在GPU上运行。</p>
+                </div>
+                <div class="border-l-4 border-blue-500 pl-4 py-2">
+                    <h3 class="font-medium">4. 迁移后模型无法运行</h3>
+                    <p class="text-sm text-gray-600">解决方案：检查CUDA版本兼容性，确保内网工作站安装了正确的NVIDIA驱动。</p>
+                </div>
+                <div class="border-l-4 border-blue-500 pl-4 py-2">
+                    <h3 class="font-medium">5. 中文输出质量不佳</h3>
+                    <p class="text-sm text-gray-600">解决方案：调整<code>temperature</code>和<code>top_p</code>参数，或使用更高量化的模型。</p>
+                </div>
+            </div>
+        </div>
+        <div class="bg-green-50 rounded-lg shadow-md p-6 mb-8 border border-green-200">
+            <h2 class="text-2xl font-semibold mb-4 text-green-800 border-b pb-2">
+                <i class="fas fa-check-circle text-green-500 mr-2"></i>部署完成
+            </h2>
+            <p class="mb-4">恭喜！您已成功在NVIDIA A6000显卡的Dell T7910内网工作站上部署了DeepSeek V3 0324模型。</p>
+            <div class="grid grid-cols-1 md:grid-cols-2 gap-4">
+                <div class="bg-white p-4 rounded-lg border border-green-100">
+                    <h3 class="font-medium text-lg mb-2 text-green-700"><i class="fas fa-lightbulb mr-2"></i>下一步建议</h3>
+                    <ul class="list-disc pl-5 space-y-1">
+                        <li>创建API服务供其他应用调用</li>
+                        <li>开发自定义前端界面</li>
+                        <li>针对特定任务进行微调</li>
+                    </ul>
+                </div>
+                <div class="bg-white p-4 rounded-lg border border-green-100">
+                    <h3 class="font-medium text-lg mb-2 text-green-700"><i class="fas fa-book mr-2"></i>学习资源</h3>
+                    <ul class="list-disc pl-5 space-y-1">
+                        <li>DeepSeek官方文档</li>
+                        <li>Unsloth GitHub仓库</li>
+                        <li>Ktransformers使用指南</li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+        <footer class="text-center text-sm text-gray-500 mt-8">
+            <p>© 2023 DeepSeek V3 0324 部署指南 | 使用Ktransformers+Unsloth联合部署方案</p>
+            <p class="mt-2">最后更新: 2023年11月</p>
+        </footer>
+    </div>
+    <script>
+        // 复制代码功能
+        function copyCode(button) {
+            const codeBlock = button.parentElement;
+            const code = codeBlock.querySelector('code').textContent;
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