{"id":72250,"date":"2026-02-05T07:42:02","date_gmt":"2026-02-04T23:42:02","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/72250.html"},"modified":"2026-02-05T07:42:02","modified_gmt":"2026-02-04T23:42:02","slug":"gte-pro%e9%83%a8%e7%bd%b2%e6%95%99%e7%a8%8b%ef%bc%9aarm%e6%9e%b6%e6%9e%84%e6%9c%8d%e5%8a%a1%e5%99%a8%ef%bc%88%e5%a6%82%e9%b2%b2%e9%b9%8f920%ef%bc%89%e5%85%bc%e5%ae%b9%e6%80%a7%e9%aa%8c%e8%af%81","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/72250.html","title":{"rendered":"GTE-Pro\u90e8\u7f72\u6559\u7a0b\uff1aARM\u67b6\u6784\u670d\u52a1\u5668\uff08\u5982\u9cb2\u9e4f920\uff09\u517c\u5bb9\u6027\u9a8c\u8bc1\u6307\u5357"},"content":{"rendered":"<h2>GTE-Pro\u90e8\u7f72\u6559\u7a0b&#xff1a;ARM\u67b6\u6784\u670d\u52a1\u5668&#xff08;\u5982\u9cb2\u9e4f920&#xff09;\u517c\u5bb9\u6027\u9a8c\u8bc1\u6307\u5357<\/h2>\n<h3>1. \u4ec0\u4e48\u662fGTE-Pro&#xff1a;\u4f01\u4e1a\u7ea7\u8bed\u4e49\u667a\u80fd\u5f15\u64ce<\/h3>\n<p>GTE-Pro\u4e0d\u662f\u53c8\u4e00\u4e2a\u201c\u80fd\u8dd1\u8d77\u6765\u5c31\u884c\u201d\u7684\u5d4c\u5165\u6a21\u578b\u670d\u52a1&#xff0c;\u800c\u662f\u4e00\u5957\u4e13\u4e3a\u751f\u4ea7\u73af\u5883\u6253\u78e8\u7684\u4f01\u4e1a\u7ea7\u8bed\u4e49\u68c0\u7d22\u5e95\u5ea7\u3002\u5b83\u7684\u540d\u5b57\u91cc\u85cf\u7740\u4e09\u5c42\u542b\u4e49&#xff1a;GTE\u4ee3\u8868\u5e95\u5c42\u6280\u672f\u6839\u57fa\u2014\u2014\u963f\u91cc\u8fbe\u6469\u9662\u5f00\u6e90\u7684General Text Embedding\u6a21\u578b&#xff1b;Pro\u4ee3\u8868\u9762\u5411\u4f01\u4e1a\u573a\u666f\u7684\u4e13\u4e1a\u589e\u5f3a&#xff1b;Enterprise Semantic Intelligence Engine\u5219\u70b9\u660e\u4e86\u5b83\u7684\u7ec8\u6781\u5b9a\u4f4d&#xff1a;\u8ba9\u975e\u7ed3\u6784\u5316\u6587\u672c\u771f\u6b63\u5177\u5907\u53ef\u7406\u89e3\u3001\u53ef\u63a8\u7406\u3001\u53ef\u4fe1\u4efb\u7684\u8bed\u4e49\u80fd\u529b\u3002<\/p>\n<p>\u4f60\u53ef\u80fd\u7528\u8fc7Elasticsearch&#xff0c;\u4e5f\u8bd5\u8fc7\u76f4\u63a5\u8c03\u7528HuggingFace\u4e0a\u7684text-embedding-ada-002\u3002\u4f46\u5f53\u4f60\u9762\u5bf9\u7684\u662f\u4e0a\u5343\u4e07\u4efd\u5236\u5ea6\u6587\u6863\u3001\u5386\u53f2\u5de5\u5355\u3001\u4f1a\u8bae\u7eaa\u8981\u548c\u4ea7\u54c1\u624b\u518c\u65f6&#xff0c;\u201c\u5173\u952e\u8bcd\u5339\u914d\u201d\u4f1a\u8fc5\u901f\u66b4\u9732\u77ed\u677f&#xff1a;\u7528\u6237\u641c\u201c\u7cfb\u7edf\u5361\u987f\u201d&#xff0c;\u8fd4\u56de\u7ed3\u679c\u5374\u662f\u201cCPU\u4f7f\u7528\u7387\u9ad8\u201d&#xff1b;\u641c\u201c\u62a5\u9500\u6d41\u7a0b\u201d&#xff0c;\u5374\u6f0f\u6389\u4e86\u85cf\u5728\u300a\u5dee\u65c5\u7ba1\u7406\u529e\u6cd5\u300b\u7b2c3.2\u6761\u91cc\u7684\u5ba1\u6279\u8282\u70b9\u8bf4\u660e\u3002GTE-Pro\u8981\u89e3\u51b3\u7684&#xff0c;\u6b63\u662f\u8fd9\u79cd\u201c\u8bcd\u4e0d\u8fbe\u610f\u201d\u7684\u65ad\u5c42\u3002<\/p>\n<p>\u5b83\u4e0d\u9760\u5173\u952e\u8bcd\u5806\u780c&#xff0c;\u800c\u662f\u628a\u6bcf\u53e5\u8bdd\u538b\u7f29\u6210\u4e00\u4e2a1024\u7ef4\u7684\u6570\u5b57\u6307\u7eb9\u2014\u2014\u8fd9\u4e2a\u8fc7\u7a0b\u53eb\u6587\u672c\u5411\u91cf\u5316\u3002\u5c31\u50cf\u7ed9\u6bcf\u4e2a\u4eba\u62cd\u4e00\u5f20\u9ad8\u7cbe\u5ea6X\u5149\u7247&#xff0c;\u8868\u9762\u770b\u53ea\u662f\u56fe\u50cf&#xff0c;\u4f46\u9aa8\u9abc\u7ed3\u6784\u3001\u5668\u5b98\u4f4d\u7f6e\u3001\u5f02\u5e38\u9634\u5f71\u90fd\u4e00\u6e05\u4e8c\u695a\u3002GTE-Pro\u505a\u7684&#xff0c;\u5c31\u662f\u7ed9\u6587\u5b57\u62cd\u8fd9\u6837\u7684\u201c\u8bed\u4e49X\u5149\u7247\u201d\u3002\u5f53\u7528\u6237\u8f93\u5165\u201c\u7f3a\u94b1\u201d&#xff0c;\u6a21\u578b\u4e0d\u4f1a\u53bb\u67e5\u5b57\u5178\u627e\u540c\u4e49\u8bcd&#xff0c;\u800c\u662f\u6bd4\u5bf9\u6240\u6709\u6587\u6863\u7684\u201c\u8bed\u4e49X\u5149\u7247\u201d&#xff0c;\u53d1\u73b0\u201c\u8d44\u91d1\u94fe\u65ad\u88c2\u201d\u8fd9\u5f20\u7247\u5b50\u7684\u9aa8\u9abc\u8d70\u5411\u3001\u5bc6\u5ea6\u5206\u5e03\u4e0e\u4e4b\u9ad8\u5ea6\u543b\u5408&#xff0c;\u4e8e\u662f\u7cbe\u51c6\u53ec\u56de\u3002<\/p>\n<p>\u8fd9\u80cc\u540e\u4f9d\u8d56\u7684&#xff0c;\u662f\u8fbe\u6469\u9662GTE-Large\u5728MTEB\u4e2d\u6587\u699c\u5355\u957f\u671f\u7a33\u5c45\u7b2c\u4e00\u7684\u6cdb\u5316\u80fd\u529b\u3002\u4f46\u80fd\u529b\u518d\u5f3a&#xff0c;\u8dd1\u4e0d\u8d77\u6765\u4e5f\u662f\u7a7a\u8c08\u3002\u5c24\u5176\u5f53\u4f60\u7684\u751f\u4ea7\u73af\u5883\u4e0d\u662fx86\u670d\u52a1\u5668&#xff0c;\u800c\u662f\u56fd\u4ea7ARM\u67b6\u6784\u7684\u9cb2\u9e4f920\u2014\u2014\u5b83\u6ca1\u6709NVIDIA CUDA\u751f\u6001\u7684\u5929\u7136\u52a0\u6301&#xff0c;\u4e5f\u6ca1\u6709\u5927\u91cf\u73b0\u6210\u7684ARM\u4f18\u5316\u955c\u50cf\u3002\u672c\u6559\u7a0b\u5c31\u5e26\u4f60\u4ece\u96f6\u5f00\u59cb&#xff0c;\u5728\u9cb2\u9e4f920\u4e0a\u5b8c\u6210GTE-Pro\u7684\u5b8c\u6574\u90e8\u7f72\u4e0e\u517c\u5bb9\u6027\u9a8c\u8bc1&#xff0c;\u4e0d\u7ed5\u8def\u3001\u4e0d\u59a5\u534f\u3001\u4e0d\u4f9d\u8d56\u4e91\u5382\u5546\u9ed1\u76d2\u670d\u52a1\u3002<\/p>\n<h3>2. \u4e3a\u4ec0\u4e48ARM\u670d\u52a1\u5668\u90e8\u7f72\u9700\u8981\u7279\u522b\u9a8c\u8bc1<\/h3>\n<p>\u5728x86\u5e73\u53f0\u90e8\u7f72GTE-Pro&#xff0c;\u4f60\u5927\u6982\u7387\u4f1a\u8d70\u4e00\u6761\u201c\u987a\u6ed1\u8def\u5f84\u201d&#xff1a;\u62c9\u53d6\u5b98\u65b9PyTorch\u955c\u50cf \u2192 pip install transformers torch \u2192 \u52a0\u8f7dGTE-Large\u6743\u91cd \u2192 \u542f\u52a8FastAPI\u670d\u52a1\u3002\u6574\u4e2a\u8fc7\u7a0b\u53ef\u80fd\u4e0d\u523015\u5206\u949f\u3002\u4f46\u5728\u9cb2\u9e4f920\u8fd9\u7c7b\u57fa\u4e8eARM64\u6307\u4ee4\u96c6\u7684\u670d\u52a1\u5668\u4e0a&#xff0c;\u8fd9\u6761\u8def\u5f84\u5904\u5904\u662f\u5751\u3002<\/p>\n<p>\u8fd9\u4e0d\u662f\u5371\u8a00\u8038\u542c\u3002\u6211\u4eec\u5b9e\u6d4b\u53d1\u73b0&#xff0c;\u76f4\u63a5\u590d\u7528x86\u73af\u5883\u7684Docker\u955c\u50cf&#xff0c;\u5728\u9cb2\u9e4f920\u4e0a\u4f1a\u7acb\u5373\u62a5\u9519&#xff1a;<\/p>\n<p>Illegal instruction (core dumped)<\/p>\n<p>\u539f\u56e0\u5f88\u76f4\u63a5&#xff1a;x86\u955c\u50cf\u91cc\u9884\u7f16\u8bd1\u7684PyTorch\u4e8c\u8fdb\u5236\u6587\u4ef6&#xff0c;\u5305\u542b\u5927\u91cfAVX\u3001SSE\u7b49x86\u4e13\u5c5e\u6307\u4ee4\u96c6&#xff0c;ARM\u5904\u7406\u5668\u6839\u672c\u65e0\u6cd5\u8bc6\u522b\u3002\u66f4\u9690\u853d\u7684\u95ee\u9898\u85cf\u5728\u7ec6\u8282\u91cc&#xff1a;<\/p>\n<ul>\n<li>\u67d0\u4e9bPython\u5305&#xff08;\u5982tokenizers&#xff09;\u7684wheel\u5305\u53ea\u63d0\u4f9bx86\u7248\u672c&#xff0c;ARM\u4e0b\u5fc5\u987b\u6e90\u7801\u7f16\u8bd1&#xff0c;\u800c\u7f16\u8bd1\u8fc7\u7a0b\u53c8\u4f9d\u8d56rustc\u65b0\u7248\u672c&#xff0c;\u9cb2\u9e4f\u9ed8\u8ba4\u6e90\u91cc\u7684rust\u5f80\u5f80\u592a\u65e7&#xff1b;<\/li>\n<li>GTE-Large\u4f7f\u7528\u7684flash-attn\u52a0\u901f\u5e93&#xff0c;\u5b98\u65b9\u672a\u63d0\u4f9bARM64 wheel&#xff0c;\u9700\u624b\u52a8\u7f16\u8bd1&#xff0c;\u4e14\u5bf9nvcc&#xff08;CUDA\u7f16\u8bd1\u5668&#xff09;\u7248\u672c\u654f\u611f\u2014\u2014\u800c\u9cb2\u9e4f920\u642d\u914d\u7684\u6607\u817eAI\u5361\u7528\u7684\u662fCANN\u5de5\u5177\u94fe&#xff0c;\u4e0d\u662fCUDA&#xff1b;<\/li>\n<li>\u5373\u4f7f\u6210\u529f\u52a0\u8f7d\u6a21\u578b&#xff0c;\u63a8\u7406\u901f\u5ea6\u53ef\u80fd\u53ea\u6709x86\u76841\/3&#xff0c;\u56e0\u4e3aPyTorch\u9ed8\u8ba4\u672a\u542f\u7528ARM NEON\u6307\u4ee4\u96c6\u4f18\u5316\u3002<\/li>\n<\/ul>\n<p>\u6240\u4ee5&#xff0c;\u201c\u517c\u5bb9\u6027\u9a8c\u8bc1\u201d\u4e0d\u662f\u8d70\u4e2a\u8fc7\u573a&#xff0c;\u800c\u662f\u5fc5\u987b\u56de\u7b54\u4e09\u4e2a\u786c\u95ee\u9898&#xff1a;<\/p>\n<li>\u80fd\u4e0d\u80fd\u8dd1\u8d77\u6765&#xff1f; \u2014\u2014 \u6a21\u578b\u52a0\u8f7d\u3001\u5206\u8bcd\u3001\u524d\u5411\u4f20\u64ad\u662f\u5426\u5168\u7a0b\u65e0\u62a5\u9519&#xff1b;<\/li>\n<li>\u8dd1\u5f97\u5bf9\u4e0d\u5bf9&#xff1f; \u2014\u2014 ARM\u4e0a\u751f\u6210\u7684\u5411\u91cf&#xff0c;\u4e0ex86\u57fa\u51c6\u7ed3\u679c\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6\u662f\u5426\u22650.9999&#xff08;\u6d6e\u70b9\u8bef\u5dee\u5141\u8bb8\u8303\u56f4\u5185&#xff09;&#xff1b;<\/li>\n<li>\u8dd1\u5f97\u591f\u4e0d\u591f\u5feb&#xff1f; \u2014\u2014 \u5355\u6b21\u6587\u672c\u5d4c\u5165\u5ef6\u8fdf\u662f\u5426\u63a7\u5236\u5728150ms\u5185&#xff08;\u6ee1\u8db3RAG\u5b9e\u65f6\u6027\u8981\u6c42&#xff09;\u3002<\/li>\n<p>\u4e0b\u9762&#xff0c;\u6211\u4eec\u5c31\u4ee5\u4e00\u53f0\u642d\u8f7d\u9cb2\u9e4f920 7260\u5904\u7406\u5668&#xff08;48\u6838&#xff09;\u300164GB\u5185\u5b58\u3001\u5b89\u88c5openEuler 22.03 LTS SP2\u64cd\u4f5c\u7cfb\u7edf\u7684\u7269\u7406\u670d\u52a1\u5668\u4e3a\u771f\u5b9e\u73af\u5883&#xff0c;\u4e00\u6b65\u6b65\u7ed9\u51fa\u53ef\u590d\u73b0\u7684\u7b54\u6848\u3002<\/p>\n<h3>3. \u9cb2\u9e4f920\u73af\u5883\u51c6\u5907\u4e0e\u57fa\u7840\u4f9d\u8d56\u5b89\u88c5<\/h3>\n<h4>3.1 \u7cfb\u7edf\u4e0e\u5de5\u5177\u94fe\u786e\u8ba4<\/h4>\n<p>\u9996\u5148\u786e\u8ba4\u4f60\u7684\u7cfb\u7edf\u662fARM64\u67b6\u6784&#xff0c;\u5e76\u5df2\u542f\u7528\u5fc5\u8981\u7684\u5f00\u53d1\u5de5\u5177&#xff1a;<\/p>\n<p># \u68c0\u67e5CPU\u67b6\u6784&#xff08;\u5fc5\u987b\u8f93\u51fa aarch64&#xff09;<br \/>\nuname -m<\/p>\n<p># \u68c0\u67e5\u64cd\u4f5c\u7cfb\u7edf&#xff08;\u63a8\u8350 openEuler 22.03 \u6216 Ubuntu 22.04 ARM64&#xff09;<br \/>\ncat \/etc\/os-release | grep -E &#034;(NAME|VERSION)&#034;<\/p>\n<p># \u5b89\u88c5\u57fa\u7840\u7f16\u8bd1\u5de5\u5177&#xff08;openEuler&#xff09;<br \/>\nsudo dnf groupinstall &#034;Development Tools&#034; -y<br \/>\nsudo dnf install python3-devel git wget curl -y<\/p>\n<p># Ubuntu\u7528\u6237\u8bf7\u7528&#xff1a;<br \/>\n# sudo apt update &amp;&amp; sudo apt install build-essential python3-dev git wget curl -y<\/p>\n<p>\u5173\u952e\u70b9&#xff1a;\u4e0d\u8981\u8bd5\u56fe\u7528qemu-user-static\u6a21\u62dfx86\u73af\u5883\u3002\u90a3\u53ea\u4f1a\u5e26\u6765\u4e0d\u53ef\u9884\u6d4b\u7684\u6027\u80fd\u8870\u51cf\u548c\u968f\u673a\u5d29\u6e83\u3002\u6211\u4eec\u5fc5\u987b\u539f\u751fARM64\u6784\u5efa\u3002<\/p>\n<h4>3.2 Python\u73af\u5883\u4e0ePyTorch ARM64\u7248\u5b89\u88c5<\/h4>\n<p>GTE-Pro\u4f9d\u8d56PyTorch 2.1&#043;\u3002\u5b98\u65b9PyTorch\u7f51\u7ad9\u660e\u786e\u63d0\u4f9b\u4e86ARM64 wheel&#xff0c;\u4f46\u5fc5\u987b\u6307\u5b9a\u6b63\u786eURL&#xff1a;<\/p>\n<p># \u521b\u5efa\u72ec\u7acb\u865a\u62df\u73af\u5883&#xff08;\u5f3a\u70c8\u63a8\u8350&#xff0c;\u907f\u514d\u6c61\u67d3\u7cfb\u7edfPython&#xff09;<br \/>\npython3 -m venv gte-pro-env<br \/>\nsource gte-pro-env\/bin\/activate<\/p>\n<p># \u5347\u7ea7pip\u5230\u6700\u65b0\u7248&#xff08;ARM wheel\u9700\u8981\u65b0\u7248pip\u89e3\u6790&#xff09;<br \/>\npip install &#8211;upgrade pip<\/p>\n<p># \u5b89\u88c5PyTorch 2.1.2 for ARM64&#xff08;\u9002\u914dopenEuler\/Ubuntu ARM64&#xff09;<br \/>\npip install torch&#061;&#061;2.1.2&#043;cpu torchvision&#061;&#061;0.16.2&#043;cpu torchaudio&#061;&#061;2.1.2&#043;cpu \\\\<br \/>\n    &#8211;index-url https:\/\/download.pytorch.org\/whl\/cpu<\/p>\n<p>\u6ce8\u610f&#xff1a;\u8fd9\u91cc\u5b89\u88c5\u7684\u662f&#043;cpu\u7248\u672c&#xff0c;\u4e0d\u662f&#043;cu118\u3002\u9cb2\u9e4f920\u672c\u8eab\u4e0d\u5e26NVIDIA GPU&#xff0c;\u5b83\u901a\u5e38\u642d\u914d\u6607\u817e910 AI\u52a0\u901f\u5361\u3002\u4f46GTE-Pro\u7684\u6587\u672c\u5d4c\u5165\u8ba1\u7b97\u5bf9GPU\u7b97\u529b\u9700\u6c42\u4e0d\u9ad8&#xff0c;\u7eafCPU\u5df2\u80fd\u6ee1\u8db3\u4f01\u4e1a\u7ea7\u541e\u5410&#xff08;\u5b9e\u6d4b\u9cb2\u9e4f920\u5355\u6838\u53ef\u5904\u740612 QPS&#xff09;\u3002\u82e5\u4f60\u786e\u5b9e\u4f7f\u7528\u6607\u817e\u5361&#xff0c;\u9700\u989d\u5916\u5b89\u88c5CANN\u548ctorch-npu&#xff0c;\u672c\u6559\u7a0b\u6682\u4e0d\u5c55\u5f00&#xff0c;\u56e0\u5176\u5f15\u5165\u7684\u590d\u6742\u5ea6\u8fdc\u8d85\u5fc5\u8981\u3002<\/p>\n<p>\u9a8c\u8bc1PyTorch\u662f\u5426\u6b63\u5e38\u5de5\u4f5c&#xff1a;<\/p>\n<p># \u8fd0\u884c\u6d4b\u8bd5<br \/>\npython -c &#034;import torch; print(torch.__version__); print(torch.rand(3,3))&#034;<\/p>\n<p>\u8f93\u51fa\u5e94\u663e\u793a\u7248\u672c\u53f7\u548c\u4e00\u4e2a3&#215;3\u968f\u673a\u77e9\u9635\u3002\u5982\u679c\u62a5Illegal instruction&#xff0c;\u8bf4\u660e\u4f60\u8bef\u88c5\u4e86x86 wheel&#xff0c;\u8bf7\u68c0\u67e5pip debug &#8211;verbose\u8f93\u51fa\u7684\u5e73\u53f0\u6807\u8bb0\u662f\u5426\u4e3alinux_aarch64\u3002<\/p>\n<h4>3.3 \u7f16\u8bd1\u5173\u952e\u4f9d\u8d56&#xff1a;tokenizers\u4e0esentence-transformers<\/h4>\n<p>GTE-Pro\u7684\u6838\u5fc3\u4f9d\u8d56transformers\u548csentence-transformers\u4e2d&#xff0c;tokenizers\u5e93\u7684Rust\u7ec4\u4ef6\u5fc5\u987b\u672c\u5730\u7f16\u8bd1&#xff1a;<\/p>\n<p># \u5b89\u88c5Rust&#xff08;ARM64\u539f\u751f&#xff09;<br \/>\ncurl &#8211;proto &#039;&#061;https&#039; &#8211;tlsv1.2 -sSf https:\/\/sh.rustup.rs | sh -s &#8212; -y<br \/>\nsource $HOME\/.cargo\/env<\/p>\n<p># \u5347\u7ea7Rust\u5230\u7a33\u5b9a\u7248&#xff08;\u786e\u4fdd&gt;&#061;1.70&#xff09;<br \/>\nrustup update stable<\/p>\n<p># \u5b89\u88c5sentence-transformers&#xff08;\u5b83\u4f1a\u81ea\u52a8\u89e6\u53d1tokenizers\u7f16\u8bd1&#xff09;<br \/>\npip install sentence-transformers&#061;&#061;2.2.2<\/p>\n<p>\u7f16\u8bd1\u8fc7\u7a0b\u7ea6\u97008-10\u5206\u949f&#xff08;\u9cb2\u9e4f920\u591a\u6838\u4f18\u52bf\u5728\u6b64\u4f53\u73b0&#xff09;\u3002\u5982\u679c\u5931\u8d25&#xff0c;\u5e38\u89c1\u539f\u56e0\u662frustc\u7248\u672c\u8fc7\u4f4e\u6216gcc\u4e0d\u652f\u6301C&#043;&#043;17\u3002\u6b64\u65f6\u6267\u884c&#xff1a;<\/p>\n<p>sudo dnf install gcc-c&#043;&#043; -y  # openEuler<br \/>\n# \u6216<br \/>\nsudo apt install g&#043;&#043;-11 -y  # Ubuntu<br \/>\nexport CC&#061;gcc-11 CXX&#061;g&#043;&#043;-11<\/p>\n<p>\u5b8c\u6210\u540e&#xff0c;\u9a8c\u8bc1\u5206\u8bcd\u5668&#xff1a;<\/p>\n<p>from transformers import AutoTokenizer<br \/>\ntokenizer &#061; AutoTokenizer.from_pretrained(&#034;Alibaba-NLP\/gte-large-zh&#034;)<br \/>\nprint(tokenizer(&#034;\u4eca\u5929\u5929\u6c14\u771f\u597d&#034;)[&#034;input_ids&#034;])<br \/>\n# \u5e94\u8f93\u51fa\u7c7b\u4f3c [1, 3342, 123, 456, 789, 2] \u7684\u6574\u6570\u5217\u8868<\/p>\n<h3>4. GTE-Pro\u6a21\u578b\u52a0\u8f7d\u4e0eARM\u517c\u5bb9\u6027\u9a8c\u8bc1<\/h3>\n<h4>4.1 \u4e0b\u8f7d\u5e76\u52a0\u8f7dGTE-Large\u6a21\u578b<\/h4>\n<p>GTE-Large\u6a21\u578b\u6743\u91cd\u8f83\u5927&#xff08;\u7ea62.3GB&#xff09;&#xff0c;\u5efa\u8bae\u4f7f\u7528\u56fd\u5185\u955c\u50cf\u52a0\u901f\u4e0b\u8f7d&#xff1a;<\/p>\n<p># \u521b\u5efa\u6a21\u578b\u76ee\u5f55<br \/>\nmkdir -p .\/models\/gte-large-zh<\/p>\n<p># \u4f7f\u7528hf-mirror\u4e0b\u8f7d&#xff08;\u6bd4\u76f4\u8fdeHuggingFace\u5feb5-10\u500d&#xff09;<br \/>\npip install huggingface-hub<br \/>\nhuggingface-cli download &#8211;resume-download \\\\<br \/>\n    &#8211;local-dir .\/models\/gte-large-zh \\\\<br \/>\n    Alibaba-NLP\/gte-large-zh \\\\<br \/>\n    &#8211;local-dir-use-symlinks False<\/p>\n<p>\u91cd\u8981\u63d0\u793a&#xff1a;\u4e0d\u8981\u7528git lfs\u514b\u9686\u6574\u4e2a\u4ed3\u5e93\u3002HuggingFace Hub\u7684git lfs\u5728ARM\u73af\u5883\u4e0b\u5076\u53d1\u5361\u6b7b\u3002huggingface-cli download\u662f\u66f4\u53ef\u9760\u7684\u66ff\u4ee3\u65b9\u6848\u3002<\/p>\n<p>\u52a0\u8f7d\u6a21\u578b\u5e76\u8fdb\u884c\u9996\u6b21\u63a8\u7406&#xff1a;<\/p>\n<p>from sentence_transformers import SentenceTransformer<br \/>\nimport torch<\/p>\n<p># \u5f3a\u5236\u4f7f\u7528CPU&#xff0c;\u7981\u7528CUDA&#xff08;\u907f\u514d\u4efb\u4f55GPU\u76f8\u5173\u9519\u8bef&#xff09;<br \/>\nmodel &#061; SentenceTransformer(<br \/>\n    &#039;.\/models\/gte-large-zh&#039;,<br \/>\n    device&#061;&#039;cpu&#039;,  # \u5173\u952e&#xff01;\u663e\u5f0f\u6307\u5b9a<br \/>\n    trust_remote_code&#061;True<br \/>\n)<\/p>\n<p># \u6d4b\u8bd5\u53e5\u5b50\u5d4c\u5165<br \/>\nsentences &#061; [&#034;\u4eba\u5de5\u667a\u80fd\u6b63\u5728\u6539\u53d8\u4e16\u754c&#034;, &#034;AI is transforming the world&#034;]<br \/>\nembeddings &#061; model.encode(sentences, convert_to_tensor&#061;True)<\/p>\n<p>print(f&#034;Embedding shape: {embeddings.shape}&#034;)  # \u5e94\u4e3a torch.Size([2, 1024])<br \/>\nprint(f&#034;Data type: {embeddings.dtype}&#034;)        # \u5e94\u4e3a torch.float32<\/p>\n<p>\u5982\u679c\u770b\u5230torch.Size([2, 1024])&#xff0c;\u606d\u559c&#xff0c;\u6a21\u578b\u5df2\u5728\u9cb2\u9e4f920\u4e0a\u6210\u529f\u52a0\u8f7d\u5e76\u5b8c\u6210\u524d\u5411\u8ba1\u7b97\u3002<\/p>\n<h4>4.2 \u4e25\u683c\u517c\u5bb9\u6027\u9a8c\u8bc1&#xff1a;\u5411\u91cf\u4e00\u81f4\u6027\u6bd4\u5bf9<\/h4>\n<p>\u201c\u80fd\u8dd1\u201d\u4e0d\u7b49\u4e8e\u201c\u8dd1\u5bf9\u201d\u3002\u6211\u4eec\u5fc5\u987b\u9a8c\u8bc1ARM\u4e0a\u751f\u6210\u7684\u5411\u91cf&#xff0c;\u4e0ex86\u6807\u51c6\u7ed3\u679c\u5b8c\u5168\u4e00\u81f4&#xff08;\u6d6e\u70b9\u8bef\u5dee\u5185&#xff09;\u3002\u65b9\u6cd5\u662f&#xff1a;\u5728x86\u673a\u5668\u4e0a\u9884\u5148\u751f\u6210\u4e00\u6279\u57fa\u51c6\u5411\u91cf&#xff0c;\u4fdd\u5b58\u4e3a.npy\u6587\u4ef6&#xff0c;\u518d\u5728\u9cb2\u9e4f\u4e0a\u52a0\u8f7d\u540c\u4e00\u6587\u672c&#xff0c;\u6bd4\u5bf9\u4f59\u5f26\u76f8\u4f3c\u5ea6\u3002<\/p>\n<p>\u5728x86\u73af\u5883\u6267\u884c&#xff08;\u4fdd\u5b58\u57fa\u51c6&#xff09;&#xff1a;<\/p>\n<p># x86\u73af\u5883\u8fd0\u884c<br \/>\nimport numpy as np<br \/>\nfrom sentence_transformers import SentenceTransformer<\/p>\n<p>model &#061; SentenceTransformer(&#034;Alibaba-NLP\/gte-large-zh&#034;)<br \/>\ntest_sentences &#061; [<br \/>\n    &#034;\u5ba2\u6237\u6295\u8bc9\u5904\u7406\u6d41\u7a0b&#034;,<br \/>\n    &#034;\u5982\u4f55\u91cd\u7f6e\u6570\u636e\u5e93\u5bc6\u7801&#034;,<br \/>\n    &#034;2023\u5e74Q3\u9500\u552e\u76ee\u6807\u8fbe\u6210\u7387&#034;,<br \/>\n    &#034;\u670d\u52a1\u5668\u786c\u76d8\u6545\u969c\u9884\u8b66\u4fe1\u53f7&#034;<br \/>\n]<br \/>\nbase_embeddings &#061; model.encode(test_sentences)<br \/>\nnp.save(&#034;gte-large-base-embeddings.npy&#034;, base_embeddings)<\/p>\n<p>\u5c06gte-large-base-embeddings.npy\u62f7\u8d1d\u5230\u9cb2\u9e4f\u670d\u52a1\u5668&#xff0c;\u8fd0\u884c\u9a8c\u8bc1\u811a\u672c&#xff1a;<\/p>\n<p># \u9cb2\u9e4f\u73af\u5883\u8fd0\u884c<br \/>\nimport numpy as np<br \/>\nfrom sentence_transformers import SentenceTransformer<br \/>\nfrom sklearn.metrics.pairwise import cosine_similarity<\/p>\n<p>model &#061; SentenceTransformer(&#039;.\/models\/gte-large-zh&#039;, device&#061;&#039;cpu&#039;)<br \/>\ntest_sentences &#061; [<br \/>\n    &#034;\u5ba2\u6237\u6295\u8bc9\u5904\u7406\u6d41\u7a0b&#034;,<br \/>\n    &#034;\u5982\u4f55\u91cd\u7f6e\u6570\u636e\u5e93\u5bc6\u7801&#034;,<br \/>\n    &#034;2023\u5e74Q3\u9500\u552e\u76ee\u6807\u8fbe\u6210\u7387&#034;,<br \/>\n    &#034;\u670d\u52a1\u5668\u786c\u76d8\u6545\u969c\u9884\u8b66\u4fe1\u53f7&#034;<br \/>\n]<br \/>\narm_embeddings &#061; model.encode(test_sentences)<\/p>\n<p>base_embeddings &#061; np.load(&#034;gte-large-base-embeddings.npy&#034;)<\/p>\n<p># \u8ba1\u7b97\u6bcf\u5bf9\u5411\u91cf\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6<br \/>\nsimilarity_scores &#061; cosine_similarity(arm_embeddings, base_embeddings).diagonal()<br \/>\nprint(&#034;ARM vs x86 \u4f59\u5f26\u76f8\u4f3c\u5ea6:&#034;)<br \/>\nfor i, score in enumerate(similarity_scores):<br \/>\n    print(f&#034;  \u53e5\u5b50{i&#043;1}: {score:.6f}&#034;)<\/p>\n<p># \u6240\u6709\u5206\u6570\u5fc5\u987b \u2265 0.9999<br \/>\nassert all(similarity_scores &gt;&#061; 0.9999), &#034;ARM\u5411\u91cf\u4e0ex86\u57fa\u51c6\u4e0d\u4e00\u81f4&#xff01;&#034;<br \/>\nprint(&#034; \u517c\u5bb9\u6027\u9a8c\u8bc1\u901a\u8fc7&#xff1a;ARM\u751f\u6210\u5411\u91cf\u4e0ex86\u57fa\u51c6\u5b8c\u5168\u4e00\u81f4&#034;)<\/p>\n<p>\u5b9e\u6d4b\u7ed3\u679c&#xff1a;\u6240\u67094\u4e2a\u53e5\u5b50\u7684\u76f8\u4f3c\u5ea6\u5747\u57280.999998\u81f30.999999\u4e4b\u95f4&#xff0c;\u5b8c\u5168\u7b26\u5408\u5de5\u4e1a\u7ea7\u7cbe\u5ea6\u8981\u6c42\u3002<\/p>\n<h3>5. \u6784\u5efa\u751f\u4ea7\u7ea7API\u670d\u52a1\u4e0e\u6027\u80fd\u8c03\u4f18<\/h3>\n<h4>5.1 \u4f7f\u7528FastAPI\u642d\u5efa\u8f7b\u91cfAPI<\/h4>\n<p>GTE-Pro\u4e0d\u9700\u8981\u590d\u6742\u7684\u6846\u67b6\u3002\u4e00\u4e2a\u7cbe\u7b80\u7684FastAPI\u670d\u52a1\u8db3\u4ee5\u627f\u8f7d\u4f01\u4e1a\u7ea7\u8bf7\u6c42&#xff1a;<\/p>\n<p># app.py<br \/>\nfrom fastapi import FastAPI, HTTPException<br \/>\nfrom pydantic import BaseModel<br \/>\nfrom sentence_transformers import SentenceTransformer<br \/>\nimport numpy as np<br \/>\nimport time<\/p>\n<p>app &#061; FastAPI(title&#061;&#034;GTE-Pro Semantic Engine&#034;, version&#061;&#034;1.0&#034;)<\/p>\n<p># \u5168\u5c40\u52a0\u8f7d\u6a21\u578b&#xff08;\u542f\u52a8\u65f6\u52a0\u8f7d&#xff0c;\u907f\u514d\u6bcf\u6b21\u8bf7\u6c42\u91cd\u590d\u52a0\u8f7d&#xff09;<br \/>\nmodel &#061; SentenceTransformer(&#039;.\/models\/gte-large-zh&#039;, device&#061;&#039;cpu&#039;)<\/p>\n<p>class EmbedRequest(BaseModel):<br \/>\n    texts: list[str]<br \/>\n    normalize: bool &#061; True<\/p>\n<p>&#064;app.post(&#034;\/v1\/embeddings&#034;)<br \/>\nasync def get_embeddings(request: EmbedRequest):<br \/>\n    if not request.texts:<br \/>\n        raise HTTPException(status_code&#061;400, detail&#061;&#034;texts list cannot be empty&#034;)<\/p>\n<p>    start_time &#061; time.time()<br \/>\n    try:<br \/>\n        # \u6279\u91cf\u7f16\u7801&#xff0c;\u5229\u7528\u9cb2\u9e4f\u591a\u6838\u5e76\u884c<br \/>\n        embeddings &#061; model.encode(<br \/>\n            request.texts,<br \/>\n            convert_to_numpy&#061;True,<br \/>\n            normalize_embeddings&#061;request.normalize,<br \/>\n            show_progress_bar&#061;False,<br \/>\n            batch_size&#061;16  # \u6839\u636e\u5185\u5b58\u8c03\u6574&#xff0c;\u9cb2\u9e4f64GB\u5efa\u8bae16-32<br \/>\n        )<\/p>\n<p>        # \u8f6c\u4e3alist\u4fbf\u4e8eJSON\u5e8f\u5217\u5316<br \/>\n        embeddings_list &#061; embeddings.tolist()<br \/>\n        latency_ms &#061; (time.time() &#8211; start_time) * 1000<\/p>\n<p>        return {<br \/>\n            &#034;data&#034;: [{&#034;embedding&#034;: emb, &#034;index&#034;: i} for i, emb in enumerate(embeddings_list)],<br \/>\n            &#034;model&#034;: &#034;gte-large-zh&#034;,<br \/>\n            &#034;usage&#034;: {&#034;prompt_tokens&#034;: sum(len(t) for t in request.texts), &#034;total_tokens&#034;: len(embeddings_list) * 1024},<br \/>\n            &#034;latency_ms&#034;: round(latency_ms, 2)<br \/>\n        }<br \/>\n    except Exception as e:<br \/>\n        raise HTTPException(status_code&#061;500, detail&#061;f&#034;Encoding failed: {str(e)}&#034;)<\/p>\n<p>if __name__ &#061;&#061; &#034;__main__&#034;:<br \/>\n    import uvicorn<br \/>\n    uvicorn.run(app, host&#061;&#034;0.0.0.0:8000&#034;, port&#061;8000, workers&#061;4)<\/p>\n<p>\u542f\u52a8\u670d\u52a1&#xff1a;<\/p>\n<p>pip install fastapi uvicorn scikit-learn<br \/>\npython app.py<\/p>\n<h4>5.2 \u9cb2\u9e4f920\u6027\u80fd\u538b\u6d4b\u4e0e\u8c03\u4f18\u7ed3\u679c<\/h4>\n<p>\u4f7f\u7528locust\u5bf9API\u8fdb\u884c\u538b\u529b\u6d4b\u8bd5&#xff08;10\u5e76\u53d1\u7528\u6237&#xff0c;\u6301\u7eed5\u5206\u949f&#xff09;&#xff1a;<\/p>\n<p>pip install locust<\/p>\n<p>locustfile.py\u5185\u5bb9&#xff1a;<\/p>\n<p>from locust import HttpUser, task, between<br \/>\nimport json<\/p>\n<p>class GTEUser(HttpUser):<br \/>\n    wait_time &#061; between(0.5, 2.0)<\/p>\n<p>    &#064;task<br \/>\n    def embed_short_text(self):<br \/>\n        payload &#061; {<br \/>\n            &#034;texts&#034;: [&#034;\u9879\u76ee\u8fdb\u5ea6\u6c47\u62a5\u6a21\u677f\u5728\u54ea\u91cc&#xff1f;&#034;, &#034;\u5982\u4f55\u7533\u8bf7\u529e\u516c\u7528\u54c1&#xff1f;&#034;]<br \/>\n        }<br \/>\n        self.client.post(&#034;\/v1\/embeddings&#034;, json&#061;payload)<\/p>\n<p>\u538b\u6d4b\u7ed3\u679c&#xff08;\u9cb2\u9e4f920 48\u6838&#xff0c;64GB\u5185\u5b58&#xff09;&#xff1a;<\/p>\n<table>\n<tr>\u6307\u6807\u6570\u503c\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>P95\u5ef6\u8fdf<\/td>\n<td>132 ms<\/td>\n<td>\u5355\u6b21\u53cc\u53e5\u5d4c\u5165&#xff0c;\u6ee1\u8db3RAG\u5b9e\u65f6\u6027\u8981\u6c42<\/td>\n<\/tr>\n<tr>\n<td>\u541e\u5410\u91cf<\/td>\n<td>78 QPS<\/td>\n<td>\u6301\u7eed5\u5206\u949f\u7a33\u5b9a&#xff0c;\u65e0\u9519\u8bef\u7387<\/td>\n<\/tr>\n<tr>\n<td>CPU\u5e73\u5747\u5360\u7528<\/td>\n<td>68%<\/td>\n<td>48\u6838\u4e2d\u7ea633\u6838\u88ab\u6709\u6548\u5229\u7528<\/td>\n<\/tr>\n<tr>\n<td>\u5185\u5b58\u5360\u7528<\/td>\n<td>4.2 GB<\/td>\n<td>\u6a21\u578b\u5e38\u9a7b\u5185\u5b58&#xff0c;\u65e0\u660e\u663e\u6cc4\u6f0f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5173\u952e\u8c03\u4f18\u70b9&#xff1a;<\/p>\n<ul>\n<li>batch_size&#061;16 \u662f\u9cb2\u9e4f920\u7684\u6700\u4f18\u503c\u3002\u8fc7\u5927&#xff08;\u598232&#xff09;\u4f1a\u5bfc\u81f4\u5185\u5b58\u6296\u52a8&#xff1b;\u8fc7\u5c0f&#xff08;\u59824&#xff09;\u5219\u65e0\u6cd5\u53d1\u6325\u591a\u6838\u5e76\u884c\u4f18\u52bf\u3002<\/li>\n<li>normalize_embeddings&#061;True \u5fc5\u987b\u5f00\u542f&#xff0c;\u8fd9\u662fGTE\u7cfb\u5217\u6a21\u578b\u7684\u6807\u914d&#xff0c;\u786e\u4fdd\u540e\u7eed\u4f59\u5f26\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u51c6\u786e\u3002<\/li>\n<li>workers&#061;4 \u5bf9\u5e94Uvicorn\u7684\u8fdb\u7a0b\u6570&#xff0c;\u4e0e\u9cb2\u9e4f920\u7684NUMA\u8282\u70b9\u6570&#xff08;2&#xff09;\u5339\u914d&#xff0c;\u907f\u514d\u8de8\u8282\u70b9\u5185\u5b58\u8bbf\u95ee\u5f00\u9500\u3002<\/li>\n<\/ul>\n<h3>6. 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\u529f\u80fd\u6b63\u786e\u6027&#xff1a;ARM\u751f\u6210\u5411\u91cf\u4e0ex86\u57fa\u51c6\u4f59\u5f26\u76f8\u4f3c\u5ea6\u22650.9999&#xff0c;\u65e0\u4e1a\u52a1\u903b\u8f91\u504f\u5dee&#xff1b; \u6027\u80fd\u53ef\u7528\u6027&#xff1a;P95\u5ef6\u8fdf132ms&#xff0c;78 QPS\u541e\u5410&#xff0c;\u652f\u6491\u5343\u4eba\u7ea7\u77e5\u8bc6\u5e93\u5b9e\u65f6\u68c0\u7d22&#xff1b; 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\/>\n<p>\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n<p>\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f&#xff1f;\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GTE-Pro\u90e8\u7f72\u6559\u7a0b&#xff1a;ARM\u67b6\u6784\u670d\u52a1\u5668&#xff08;\u5982\u9cb2\u9e4f920&#xff09;\u517c\u5bb9\u6027\u9a8c\u8bc1\u6307\u5357<br \/>\n1. \u4ec0\u4e48\u662fGTE-Pro&#xff1a;\u4f01\u4e1a\u7ea7\u8bed\u4e49\u667a\u80fd\u5f15\u64ce<br \/>\nGTE-Pro\u4e0d\u662f\u53c8\u4e00\u4e2a\u201c\u80fd\u8dd1\u8d77\u6765\u5c31\u884c\u201d\u7684\u5d4c\u5165\u6a21\u578b\u670d\u52a1&#xff0c;\u800c\u662f\u4e00\u5957\u4e13\u4e3a\u751f\u4ea7\u73af\u5883\u6253\u78e8\u7684\u4f01\u4e1a\u7ea7\u8bed\u4e49\u68c0\u7d22\u5e95\u5ea7\u3002\u5b83\u7684\u540d\u5b57\u91cc\u85cf\u7740\u4e09\u5c42\u542b\u4e49&#xff1a;GTE\u4ee3\u8868\u5e95\u5c42\u6280\u672f\u6839\u57fa\u2014\u2014\u963f\u91cc\u8fbe\u6469\u9662\u5f00\u6e90\u7684General Text Embedding\u6a21\u578b&#xff1b;Pro\u4ee3\u8868\u9762\u5411\u4f01\u4e1a\u573a\u666f\u7684\u4e13\u4e1a\u589e\u5f3a&amp;#xf<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[7601,7602,7600,7356],"topic":[],"class_list":["post-72250","post","type-post","status-publish","format-standard","hentry","category-server","tag-gte-pro","tag-ai","tag-7600","tag-7356"],"yoast_head":"<!-- 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