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<div class="container mx-auto px-4 py-8 max-w-5xl"> |
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<header class="mb-8 text-center"> |
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<h1 class="text-4xl font-bold text-green-700 mb-2">DeepSeek V3 0324 部署指南</h1> |
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<p class="text-xl text-gray-600">NVIDIA A6000 + Dell T7910 内网工作站</p> |
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<div class="flex justify-center mt-4"> |
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<span class="bg-green-100 text-green-800 text-sm font-medium px-2.5 py-0.5 rounded mr-2">Ktransformers</span> |
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<span class="bg-blue-100 text-blue-800 text-sm font-medium px-2.5 py-0.5 rounded mr-2">Unsloth</span> |
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<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> |
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</div> |
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</header> |
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<div class="bg-white rounded-lg shadow-md p-6 mb-8"> |
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<h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2"> |
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<i class="fas fa-info-circle text-blue-500 mr-2"></i>部署方案概述 |
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</h2> |
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<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> |
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<div class="grid grid-cols-1 md:grid-cols-2 gap-4 mb-6"> |
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<div class="bg-gray-50 p-4 rounded-lg"> |
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<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> |
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<ul class="list-disc pl-5 space-y-1"> |
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<li>在可访问外网的Windows电脑上使用WSL</li> |
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<li>完成所有依赖项的安装</li> |
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<li>下载模型文件和配置</li> |
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</ul> |
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</div> |
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<div class="bg-gray-50 p-4 rounded-lg"> |
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<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> |
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<ul class="list-disc pl-5 space-y-1"> |
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<li>将完整环境复制到固态硬盘</li> |
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<li>插入内网工作站启动</li> |
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<li>验证模型运行</li> |
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</ul> |
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</div> |
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</div> |
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<div class="note"> |
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<i class="fas fa-lightbulb text-yellow-500 mr-2"></i> |
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<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。 |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow-md p-6 mb-8"> |
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<h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2"> |
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<i class="fas fa-download text-purple-500 mr-2"></i>准备工作 |
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</h2> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 系统要求</h3> |
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<div class="hardware-req"> |
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<div class="grid grid-cols-1 md:grid-cols-2 gap-4"> |
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<div> |
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<h4 class="font-medium mb-2"><i class="fas fa-desktop mr-2"></i>硬件配置</h4> |
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<ul class="list-disc pl-5 space-y-1"> |
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<li>NVIDIA A6000显卡 (48GB显存)</li> |
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<li>Dell Precision T7910工作站</li> |
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<li>至少64GB系统内存</li> |
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<li>高速固态硬盘(建议NVMe)</li> |
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</ul> |
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</div> |
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<div> |
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<h4 class="font-medium mb-2"><i class="fas fa-cog mr-2"></i>软件环境</h4> |
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<ul class="list-disc pl-5 space-y-1"> |
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<li>Windows 10/11 with WSL2</li> |
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<li>Ubuntu 20.04/22.04 LTS (WSL)</li> |
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<li>Python 3.10+</li> |
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<li>CUDA 11.8/12.1</li> |
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</ul> |
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</div> |
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</div> |
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</div> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 模型选择</h3> |
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<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> |
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<div class="overflow-x-auto"> |
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<table class="min-w-full bg-white border border-gray-200"> |
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<thead class="bg-gray-100"> |
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<tr> |
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<th class="py-2 px-4 border-b">模型名称</th> |
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<th class="py-2 px-4 border-b">量化级别</th> |
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<th class="py-2 px-4 border-b">显存需求</th> |
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<th class="py-2 px-4 border-b">内存需求</th> |
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<th class="py-2 px-4 border-b">适用场景</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td class="py-2 px-4 border-b">deepseek-v3-0324-Q2_K.gguf</td> |
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<td class="py-2 px-4 border-b">Q2_K (极低精度)</td> |
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<td class="py-2 px-4 border-b">~12GB</td> |
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<td class="py-2 px-4 border-b">32GB+</td> |
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<td class="py-2 px-4 border-b">快速推理，低资源</td> |
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</tr> |
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<tr class="bg-gray-50"> |
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<td class="py-2 px-4 border-b">deepseek-v3-0324-Q4_K_M.gguf</td> |
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<td class="py-2 px-4 border-b">Q4_K_M (中等精度)</td> |
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<td class="py-2 px-4 border-b">~18GB</td> |
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<td class="py-2 px-4 border-b">48GB+</td> |
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<td class="py-2 px-4 border-b">平衡精度与速度</td> |
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</tr> |
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<tr> |
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<td class="py-2 px-4 border-b">deepseek-v3-0324-Q5_K_M.gguf</td> |
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<td class="py-2 px-4 border-b">Q5_K_M (较高精度)</td> |
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<td class="py-2 px-4 border-b">~22GB</td> |
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<td class="py-2 px-4 border-b">64GB+</td> |
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<td class="py-2 px-4 border-b">高质量推理</td> |
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</tr> |
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<tr class="bg-gray-50"> |
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<td class="py-2 px-4 border-b">deepseek-v3-0324-Q6_K.gguf</td> |
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<td class="py-2 px-4 border-b">Q6_K (高精度)</td> |
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<td class="py-2 px-4 border-b">~26GB</td> |
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<td class="py-2 px-4 border-b">64GB+</td> |
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<td class="py-2 px-4 border-b">最高质量推理</td> |
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</tr> |
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</tbody> |
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</table> |
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</div> |
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<div class="note mt-4"> |
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<i class="fas fa-lightbulb text-yellow-500 mr-2"></i> |
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<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。 |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow-md p-6 mb-8"> |
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<h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2"> |
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<i class="fas fa-terminal text-green-500 mr-2"></i>外网环境部署 (WSL) |
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</h2> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 设置WSL环境</h3> |
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<p class="mb-4">在Windows电脑上启用WSL并安装Ubuntu:</p> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 以管理员身份打开PowerShell |
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wsl --install -d Ubuntu-22.04 |
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wsl --set-version Ubuntu-22.04 2 |
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wsl -d Ubuntu-22.04</code> |
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</div> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 配置Ubuntu环境</h3> |
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<p class="mb-4">在WSL的Ubuntu中执行以下命令:</p> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 更新系统并安装基础工具 |
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sudo apt update && sudo apt upgrade -y |
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sudo apt install -y build-essential cmake git wget python3-pip python3-venv |
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# 配置国内源 (阿里云) |
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sudo sed -i 's|http://archive.ubuntu.com|https://mirrors.aliyun.com|g' /etc/apt/sources.list |
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sudo sed -i 's|http://security.ubuntu.com|https://mirrors.aliyun.com|g' /etc/apt/sources.list |
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# 安装CUDA工具包 (使用国内源) |
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wget https://developer.download.nvidia.cn/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin |
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sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600 |
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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 |
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sudo dpkg -i cuda-repo-wsl-ubuntu-12-1-local_12.1.1-1_amd64.deb |
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sudo cp /var/cuda-repo-wsl-ubuntu-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/ |
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sudo apt-get update |
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sudo apt-get -y install cuda |
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# 验证CUDA安装 |
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nvidia-smi |
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nvcc --version</code> |
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</div> |
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<div class="note mt-4"> |
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<i class="fas fa-lightbulb text-yellow-500 mr-2"></i> |
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<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。 |
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</div> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">3. 创建Python虚拟环境</h3> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 创建虚拟环境 |
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python3 -m venv deepseek-env |
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source deepseek-env/bin/activate |
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# 配置pip国内源 |
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pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple |
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pip install --upgrade pip |
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# 安装基础依赖 |
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 |
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pip install transformers accelerate sentencepiece ninja</code> |
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</div> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">4. 安装Ktransformers和Unsloth</h3> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 安装Ktransformers |
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pip install ktransformers |
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# 安装Unsloth (使用国内Git镜像) |
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git clone https://gitee.com/mirrors/unsloth.git |
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cd unsloth |
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pip install -e . |
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# 或者直接从PyPI安装 (可能较慢) |
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# pip install unsloth</code> |
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</div> |
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<div class="warning mt-4"> |
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<i class="fas fa-exclamation-triangle text-orange-500 mr-2"></i> |
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<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。 |
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</div> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">5. 下载DeepSeek V3 0324模型</h3> |
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<p class="mb-4">从ModelScope下载GGUF量化模型:</p> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 创建模型目录 |
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mkdir -p ~/models/deepseek-v3-0324 |
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cd ~/models/deepseek-v3-0324 |
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# 使用国内镜像下载模型 (以Q5_K_M为例) |
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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 |
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# 可选: 下载其他量化级别的模型 |
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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 |
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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> |
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</div> |
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<div class="note mt-4"> |
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<i class="fas fa-lightbulb text-yellow-500 mr-2"></i> |
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<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。 |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow-md p-6 mb-8"> |
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<h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2"> |
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<i class="fas fa-cogs text-blue-500 mr-2"></i>模型配置与测试 |
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</h2> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 创建测试脚本</h3> |
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<p class="mb-4">创建一个Python脚本测试模型是否能正常运行:</p> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 创建测试脚本 |
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cat << 'EOF' > test_deepseek.py |
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from unsloth import FastLanguageModel |
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import torch |
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model_path = "/home/yourusername/models/deepseek-v3-0324/deepseek-v3-0324-Q5_K_M.gguf" |
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model, tokenizer = FastLanguageModel.from_pretrained(model_path) |
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# 配置模型参数 |
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FastLanguageModel.for_inference(model) |
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model.config.use_cache = True |
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model.config.max_seq_length = 4096 # 根据显存调整 |
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# 测试推理 |
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inputs = tokenizer("你好，DeepSeek V3！", return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=64) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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EOF |
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# 运行测试脚本 |
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python test_deepseek.py</code> |
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</div> |
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 优化配置参数</h3> |
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<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> |
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<div class="tabs mb-4"> |
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<button class="tab-button active" onclick="openTab(event, 'q5-tab')">Q5_K_M 配置</button> |
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<button class="tab-button" onclick="openTab(event, 'q6-tab')">Q6_K 配置</button> |
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<button class="tab-button" onclick="openTab(event, 'q4-tab')">Q4_K_M 配置</button> |
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</div> |
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<div id="q5-tab" class="tab-content active"> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># Q5_K_M 量化模型配置 (推荐) |
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model_config = { |
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"model_path": "/path/to/deepseek-v3-0324-Q5_K_M.gguf", |
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"n_gpu_layers": 40, # 使用尽可能多的GPU层 |
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"n_ctx": 4096, # 上下文长度 |
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"n_batch": 512, # 批处理大小 |
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"n_threads": 12, # CPU线程数(根据CPU核心数调整) |
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"max_new_tokens": 1024, |
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"temperature": 0.7, |
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"top_p": 0.9, |
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"repetition_penalty": 1.1, |
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}</code> |
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</div> |
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</div> |
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<div id="q6-tab" class="tab-content"> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># Q6_K 量化模型配置 (高质量) |
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model_config = { |
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"model_path": "/path/to/deepseek-v3-0324-Q6_K.gguf", |
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"n_gpu_layers": 35, # 减少GPU层数以适应更大模型 |
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"n_ctx": 4096, |
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"n_batch": 384, # 减小批处理大小 |
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"n_threads": 12, |
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"max_new_tokens": 768, |
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"temperature": 0.7, |
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"top_p": 0.9, |
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"repetition_penalty": 1.1, |
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}</code> |
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</div> |
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</div> |
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<div id="q4-tab" class="tab-content"> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># Q4_K_M 量化模型配置 (高性能) |
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model_config = { |
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"model_path": "/path/to/deepseek-v3-0324-Q4_K_M.gguf", |
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"n_gpu_layers": 45, # 可以使用更多GPU层 |
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"n_ctx": 4096, |
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"n_batch": 768, # 增大批处理大小 |
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"n_threads": 12, |
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"max_new_tokens": 2048, |
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"temperature": 0.7, |
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"top_p": 0.9, |
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"repetition_penalty": 1.1, |
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}</code> |
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</div> |
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</div> |
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<div class="success mt-4"> |
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<i class="fas fa-check-circle text-green-500 mr-2"></i> |
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<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。 |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow-md p-6 mb-8"> |
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<h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2"> |
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<i class="fas fa-exchange-alt text-purple-500 mr-2"></i>迁移到内网工作站 |
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</h2> |
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|
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 准备迁移内容</h3> |
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<p class="mb-4">将以下内容复制到固态硬盘:</p> |
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<ul class="list-disc pl-5 space-y-1 mb-4"> |
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<li>完整的WSL Ubuntu系统 (导出为tar文件)</li> |
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<li>模型文件 (~/models/deepseek-v3-0324/)</li> |
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<li>Python虚拟环境 (~/deepseek-env/)</li> |
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<li>测试脚本和配置文件</li> |
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</ul> |
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|
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 在WSL中导出Ubuntu系统 |
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wsl --export Ubuntu-22.04 ubuntu-22.04-deepseek.tar |
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|
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# 复制模型文件和虚拟环境 |
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cp -r ~/models /mnt/e/deepseek-deploy/ |
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cp -r ~/deepseek-env /mnt/e/deepseek-deploy/ |
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cp test_deepseek.py /mnt/e/deepseek-deploy/</code> |
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</div> |
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|
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 在内网工作站上设置</h3> |
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<p class="mb-4">将固态硬盘插入内网工作站后执行以下步骤:</p> |
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|
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 1. 安装WSL (如果尚未安装) |
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wsl --install |
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|
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# 2. 导入Ubuntu系统 |
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wsl --import Ubuntu-22.04-deepseek C:\WSL\Ubuntu-22.04-deepseek E:\ubuntu-22.04-deepseek.tar |
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|
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# 3. 设置默认用户 (替换yourusername) |
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ubuntu2204.exe config --default-user yourusername |
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|
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# 4. 启动WSL |
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wsl -d Ubuntu-22.04-deepseek</code> |
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</div> |
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|
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">3. 验证内网环境</h3> |
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<p class="mb-4">在内网工作站的WSL中验证环境:</p> |
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|
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code># 激活虚拟环境 |
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source /mnt/e/deepseek-deploy/deepseek-env/bin/activate |
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|
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# 验证CUDA |
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nvidia-smi |
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nvcc --version |
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|
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# 运行测试脚本 |
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python /mnt/e/deepseek-deploy/test_deepseek.py</code> |
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</div> |
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|
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<div class="warning mt-4"> |
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<i class="fas fa-exclamation-triangle text-orange-500 mr-2"></i> |
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<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。 |
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</div> |
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</div> |
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<div class="bg-white rounded-lg shadow-md p-6 mb-8"> |
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<h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2"> |
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<i class="fas fa-rocket text-red-500 mr-2"></i>高级配置与优化 |
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</h2> |
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|
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">1. 使用Ktransformers加速</h3> |
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<p class="mb-4">结合Ktransformers可以进一步提高推理速度:</p> |
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|
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code>from ktransformers import AutoModelForCausalLM |
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from unsloth import FastLanguageModel |
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|
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# 加载模型 |
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model_path = "/path/to/deepseek-v3-0324-Q5_K_M.gguf" |
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model, tokenizer = FastLanguageModel.from_pretrained(model_path) |
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|
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# 转换为Ktransformers格式 |
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kmodel = AutoModelForCausalLM.from_pretrained(model, device_map="auto") |
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|
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# 配置生成参数 |
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generation_config = { |
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"max_new_tokens": 1024, |
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"do_sample": True, |
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"temperature": 0.7, |
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"top_p": 0.9, |
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} |
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|
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# 推理示例 |
|
inputs = tokenizer("中国的首都是", return_tensors="pt").to("cuda") |
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outputs = kmodel.generate(**inputs, **generation_config) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))</code> |
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</div> |
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|
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">2. 批处理推理优化</h3> |
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<p class="mb-4">利用A6000的大显存进行批处理推理:</p> |
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code>def batch_inference(queries, model, tokenizer, batch_size=4): |
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# 编码所有查询 |
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inputs = tokenizer(queries, return_tensors="pt", padding=True, truncation=True).to("cuda") |
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|
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# 分批处理 |
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outputs = [] |
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for i in range(0, len(queries), batch_size): |
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batch = {k: v[i:i+batch_size] for k, v in inputs.items()} |
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batch_outputs = model.generate(**batch, max_new_tokens=256) |
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outputs.extend(tokenizer.batch_decode(batch_outputs, skip_special_tokens=True)) |
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return outputs |
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|
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# 示例使用 |
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queries = [ |
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"解释人工智能的基本概念", |
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"写一首关于春天的诗", |
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"Python中如何实现快速排序?", |
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"中国的四大发明是什么?" |
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] |
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results = batch_inference(queries, kmodel, tokenizer) |
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for q, r in zip(queries, results): |
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print(f"Q: {q}\nA: {r}\n{'='*50}")</code> |
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</div> |
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|
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<h3 class="text-xl font-medium mt-6 mb-3 text-gray-700">3. 性能监控脚本</h3> |
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<p class="mb-4">监控GPU使用情况和推理速度:</p> |
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|
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<div class="code-block"> |
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<button class="copy-btn" onclick="copyCode(this)">复制</button> |
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<code>import torch |
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from pynvml import * |
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|
|
def print_gpu_utilization(): |
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nvmlInit() |
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handle = nvmlDeviceGetHandleByIndex(0) |
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info = nvmlDeviceGetMemoryInfo(handle) |
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print(f"GPU内存使用: {info.used//1024**2}MB / {info.total//1024**2}MB") |
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print(f"GPU利用率: {nvmlDeviceGetUtilizationRates(handle).gpu}%") |
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|
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# 在推理前后调用 |
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print_gpu_utilization() |
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inputs = tokenizer("监控GPU使用情况", return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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print_gpu_utilization()</code> |
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</div> |
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</div> |
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|
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<div class="bg-white rounded-lg shadow-md p-6 mb-8"> |
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<h2 class="text-2xl font-semibold mb-4 text-gray-800 border-b pb-2"> |
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<i class="fas fa-question-circle text-indigo-500 mr-2"></i>常见问题解决 |
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</h2> |
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|
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<div class="space-y-4"> |
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<div class="border-l-4 border-blue-500 pl-4 py-2"> |
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<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> |
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</div> |
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|
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<div class="border-l-4 border-blue-500 pl-4 py-2"> |
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<h3 class="font-medium">2. 模型加载缓慢</h3> |
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<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> |
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</div> |
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|
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<div class="border-l-4 border-blue-500 pl-4 py-2"> |
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<h3 class="font-medium">3. 推理速度不理想</h3> |
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<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> |
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</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> |
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</div> |
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</div> |
|
</div> |
|
|
|
<div class="bg-green-50 rounded-lg shadow-md p-6 mb-8 border border-green-200"> |
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<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> |
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<p class="mt-2">最后更新: 2023年11月</p> |
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