{"id":278,"date":"2025-01-28T20:27:22","date_gmt":"2025-01-28T12:27:22","guid":{"rendered":"https:\/\/www.onetts.com\/ai\/?p=278"},"modified":"2025-01-28T21:10:26","modified_gmt":"2025-01-28T13:10:26","slug":"deepseek-r1","status":"publish","type":"post","link":"https:\/\/www.onetts.com\/ai\/models\/deepseek-r1\/","title":{"rendered":"DeepSeek-R1"},"content":{"rendered":"<p>DeepSeek R1\u662f\u7531DeepSeek\u56e2\u961f\u5f00\u53d1\u7684\u7b2c\u4e00\u4ee3\u63a8\u7406\u6a21\u578b\uff0c\u65e8\u5728\u901a\u8fc7\u5f3a\u5316\u5b66\u4e60\uff08Reinforcement Learning, RL\uff09\u63d0\u5347\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u7684\u63a8\u7406\u80fd\u529b\u3002\u8be5\u6a21\u578b\u7cfb\u5217\u5305\u62ecDeepSeek-R1-Zero\u548cDeepSeek-R1\u4e24\u4e2a\u7248\u672c\uff0c\u4ee5\u53ca\u57fa\u4e8eQwen\u548cLlama\u84b8\u998f\u51fa\u7684\u516d\u4e2a\u5bc6\u96c6\u6a21\u578b\u3002<\/p>\n<h3>\u6a21\u578b\u8bbe\u8ba1\u4e0e\u8bad\u7ec3\u65b9\u6cd5<\/h3>\n<p>DeepSeek R1\u7684\u8bbe\u8ba1\u7406\u5ff5\u662f\u901a\u8fc7\u5f3a\u5316\u5b66\u4e60\u6765\u6fc0\u52b1\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\uff0c\u800c\u4e0d\u662f\u4f9d\u8d56\u4f20\u7edf\u7684\u76d1\u7763\u5fae\u8c03\uff08Supervised Fine-Tuning, SFT\uff09\u3002DeepSeek-R1-Zero\u662f\u8be5\u7cfb\u5217\u7684\u521d\u6b65\u7248\u672c\uff0c\u5b83\u76f4\u63a5\u5728\u57fa\u7840\u6a21\u578b\u4e0a\u5e94\u7528\u5f3a\u5316\u5b66\u4e60\uff0c\u65e0\u9700\u9884\u5148\u8fdb\u884c\u76d1\u7763\u5fae\u8c03\u3002\u8fd9\u79cd\u65b9\u6cd5\u4f7f\u6a21\u578b\u80fd\u591f\u63a2\u7d22\u94fe\u5f0f\u601d\u7ef4\uff08Chain of Thought, CoT\uff09\uff0c\u4ece\u800c\u5728\u89e3\u51b3\u590d\u6742\u95ee\u9898\u65f6\u8868\u73b0\u51fa\u8272\u3002\u7136\u800c\uff0cDeepSeek-R1-Zero\u4e5f\u5b58\u5728\u4e00\u4e9b\u95ee\u9898\uff0c\u5982\u65e0\u9650\u91cd\u590d\u3001\u53ef\u8bfb\u6027\u5dee\u548c\u8bed\u8a00\u6df7\u5408\u3002<\/p>\n<p>\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\u5e76\u8fdb\u4e00\u6b65\u63d0\u5347\u63a8\u7406\u6027\u80fd\uff0cDeepSeek\u56e2\u961f\u5f00\u53d1\u4e86DeepSeek-R1\u3002\u8be5\u6a21\u578b\u5728\u5f3a\u5316\u5b66\u4e60\u4e4b\u524d\u52a0\u5165\u4e86\u51b7\u542f\u52a8\u6570\u636e\uff0c\u5e76\u901a\u8fc7\u591a\u9636\u6bb5\u8bad\u7ec3\u6765\u4f18\u5316\u63a8\u7406\u6a21\u5f0f\u3002DeepSeek-R1\u7684\u8bad\u7ec3\u6d41\u7a0b\u5305\u62ec\u4e24\u4e2a\u5f3a\u5316\u5b66\u4e60\u9636\u6bb5\uff0c\u7528\u4e8e\u53d1\u73b0\u66f4\u4f18\u7684\u63a8\u7406\u6a21\u5f0f\u5e76\u4f7f\u5176\u4e0e\u4eba\u7c7b\u504f\u597d\u5bf9\u9f50\uff0c\u4ee5\u53ca\u4e24\u4e2a\u76d1\u7763\u5fae\u8c03\u9636\u6bb5\uff0c\u4e3a\u6a21\u578b\u7684\u63a8\u7406\u548c\u975e\u63a8\u7406\u80fd\u529b\u63d0\u4f9b\u521d\u59cb\u79cd\u5b50\u3002<\/p>\n<h3>\u6a21\u578b\u6027\u80fd\u4e0e\u7279\u70b9<\/h3>\n<p>DeepSeek R1\u5728\u63a8\u7406\u4efb\u52a1\u4e0a\u7684\u8868\u73b0\u4e0eOpenAI\u7684o1\u76f8\u5f53\u3002\u5b83\u5728\u6570\u5b66\u3001\u4ee3\u7801\u548c\u63a8\u7406\u4efb\u52a1\u4e0a\u5c55\u73b0\u51fa\u5f3a\u5927\u7684\u80fd\u529b\uff0c\u5c24\u5176\u662f\u5728\u957f\u94fe\u63a8\u7406\uff08CoT\uff09\u65b9\u9762\u3002DeepSeek-R1-Distill-Qwen-32B\u5728\u591a\u4e2a\u57fa\u51c6\u6d4b\u8bd5\u4e2d\u8d85\u8d8a\u4e86OpenAI-o1-mini\uff0c\u6210\u4e3a\u5bc6\u96c6\u6a21\u578b\u4e2d\u7684\u65b0\u6700\u5148\u8fdb\u7ed3\u679c\u3002<br \/>\nDeepSeek R1\u7684\u53e6\u4e00\u4e2a\u91cd\u8981\u7279\u70b9\u662f\u5176\u9ad8\u6548\u7684\u67b6\u6784\u548c\u8f83\u4f4e\u7684\u786c\u4ef6\u8981\u6c42\u3002\u8be5\u6a21\u578b\u80fd\u591f\u5728\u8f83\u5c11\u7684GPU\u6216\u9ad8\u7ea7CPU\u96c6\u7fa4\u4e0a\u8fd0\u884c\uff0c\u964d\u4f4e\u4e86\u521d\u521b\u516c\u53f8\u3001\u4e2a\u4eba\u5f00\u53d1\u8005\u548c\u5f00\u6e90\u793e\u533a\u7684\u4f7f\u7528\u95e8\u69db\u3002\u6b64\u5916\uff0cDeepSeek R1\u9075\u5faaMIT License\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u84b8\u998f\u6280\u672f\u57fa\u4e8eR1\u8bad\u7ec3\u5176\u4ed6\u6a21\u578b\u3002<\/p>\n<h3>\u5f00\u6e90\u4e0e\u793e\u533a\u652f\u6301<\/h3>\n<p>DeepSeek\u56e2\u961f\u81f4\u529b\u4e8e\u63a8\u52a8\u5f00\u6e90\u751f\u6001\u7684\u53d1\u5c55\uff0c\u4e3a\u7814\u7a76\u793e\u533a\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u8d44\u6e90\u3002DeepSeek-R1-Zero\u3001DeepSeek-R1\u4ee5\u53ca\u516d\u4e2a\u57fa\u4e8eQwen\u548cLlama\u84b8\u998f\u51fa\u7684\u5bc6\u96c6\u6a21\u578b\u5747\u5df2\u5f00\u6e90\u3002\u8fd9\u4e9b\u6a21\u578b\u7684\u5f00\u6e90\u4e0d\u4ec5\u6709\u52a9\u4e8e\u7814\u7a76\u4eba\u5458\u8fdb\u4e00\u6b65\u63a2\u7d22\u548c\u6539\u8fdb\u6a21\u578b\uff0c\u4e5f\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u4f7f\u7528\u548c\u5b9a\u5236\u9009\u9879\u3002<br \/>\nDeepSeek R1\u7684\u53d1\u5e03\u6807\u5fd7\u7740\u56fd\u4ea7AI\u6280\u672f\u7684\u91cd\u5927\u7a81\u7834\u3002\u5176\u5f3a\u5927\u7684\u63a8\u7406\u80fd\u529b\u3001\u5f00\u6e90\u751f\u6001\u4ee5\u53ca\u9ad8\u6027\u4ef7\u6bd4\u7684API\u670d\u52a1\uff0c\u4e3a\u5168\u7403\u5f00\u53d1\u8005\u548c\u4f01\u4e1a\u63d0\u4f9b\u4e86\u5168\u65b0\u7684\u9009\u62e9\u3002\u968f\u7740R1\u53ca\u5176\u84b8\u998f\u7248\u672c\u7684\u5e7f\u6cdb\u5e94\u7528\uff0cAI\u6280\u672f\u7684\u666e\u53ca\u4e0e\u521b\u65b0\u5c06\u8fce\u6765\u65b0\u7684\u9ad8\u6f6e<\/p>\n<h3><strong>\u8bc4\u6d4b\u80cc\u666f\u4e0e\u76ee\u6807<\/strong><\/h3>\n<p>DeepSeek R1\u4f5c\u4e3a\u4e00\u6b3e\u4e13\u6ce8\u4e8e\u63a8\u7406\u80fd\u529b\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\uff0c\u5176\u8bc4\u6d4b\u7684\u76ee\u6807\u662f\u5168\u9762\u8bc4\u4f30\u5176\u5728\u4e0d\u540c\u4efb\u52a1\u573a\u666f\u4e2d\u7684\u8868\u73b0\uff0c\u5c24\u5176\u662f\u4e0e\u63a8\u7406\u76f8\u5173\u7684\u4efb\u52a1\u3002\u5f53\u524dLLM\u5e02\u573a\u4e2d\uff0c\u63a8\u7406\u80fd\u529b\u5df2\u6210\u4e3a\u8861\u91cf\u6a21\u578b\u6027\u80fd\u7684\u5173\u952e\u6307\u6807\u4e4b\u4e00\uff0c\u5c24\u5176\u5728\u590d\u6742\u95ee\u9898\u89e3\u51b3\u3001\u6570\u5b66\u8ba1\u7b97\u3001\u903b\u8f91\u63a8\u7406\u7b49\u65b9\u9762\u3002\u6b64\u6b21\u8bc4\u6d4b\u65e8\u5728\u9a8c\u8bc1DeepSeek R1\u662f\u5426\u80fd\u6ee1\u8db3\u8fd9\u4e9b\u9700\u6c42\uff0c\u5e76\u4e0e\u5e02\u573a\u4e0a\u7684\u4e3b\u6d41\u6a21\u578b\u8fdb\u884c\u5bf9\u6bd4\u3002<\/p>\n<hr \/>\n<h3><strong>\u8bc4\u6d4b\u7ef4\u5ea6\u4e0e\u65b9\u6cd5<\/strong><\/h3>\n<h4><strong>1. \u63a8\u7406\u80fd\u529b<\/strong><\/h4>\n<ul>\n<li><strong>\u6570\u5b66\u63a8\u7406<\/strong><br \/>\n\u901a\u8fc7\u4e00\u7cfb\u5217\u6570\u5b66\u95ee\u9898\uff08\u5305\u62ec\u57fa\u7840\u7b97\u672f\u3001\u4ee3\u6570\u3001\u51e0\u4f55\u7b49\uff09\u6d4b\u8bd5\u6a21\u578b\u7684\u6570\u5b66\u63a8\u7406\u80fd\u529b\uff0c\u8bc4\u4f30\u89e3\u9898\u7684\u51c6\u786e\u6027\u3001\u6b65\u9aa4\u5408\u7406\u6027\u4ee5\u53ca\u5904\u7406\u590d\u6742\u95ee\u9898\u7684\u80fd\u529b\u3002<\/li>\n<li><strong>\u903b\u8f91\u63a8\u7406<\/strong><br \/>\n\u4f7f\u7528\u903b\u8f91\u63a8\u7406\u9898\uff08\u5982\u4e09\u6bb5\u8bba\u3001\u7c7b\u6bd4\u63a8\u7406\uff09\u8bc4\u4f30\u6a21\u578b\u7684\u903b\u8f91\u601d\u7ef4\u80fd\u529b\uff0c\u89c2\u5bdf\u5176\u7406\u89e3\u95ee\u9898\u5e76\u5f97\u51fa\u7ed3\u8bba\u7684\u8868\u73b0\u3002<\/li>\n<li><strong>\u957f\u94fe\u63a8\u7406\uff08Chain of Thought, CoT\uff09<\/strong><br \/>\n\u6d4b\u8bd5\u6a21\u578b\u5728\u89e3\u51b3\u591a\u6b65\u9aa4\u95ee\u9898\u4e2d\u7684\u8868\u73b0\uff0c\u5982\u590d\u6742\u903b\u8f91\u94fe\u6216\u591a\u6b65\u8ba1\u7b97\uff0c\u8bc4\u4f30\u63a8\u7406\u94fe\u7684\u5b8c\u6574\u6027\u3001\u51c6\u786e\u6027\u4ee5\u53ca\u907f\u514d\u91cd\u590d\u548c\u9519\u8bef\u7684\u80fd\u529b\u3002<\/li>\n<\/ul>\n<h4><strong>2. \u8bed\u8a00\u751f\u6210\u8d28\u91cf<\/strong><\/h4>\n<ul>\n<li><strong>\u6587\u672c\u6d41\u7545\u6027<\/strong><br \/>\n\u901a\u8fc7BLEU\u3001ROUGE\u7b49\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\u8bc4\u4f30\u751f\u6210\u6587\u672c\u7684\u8fde\u8d2f\u6027\u548c\u53ef\u8bfb\u6027\u3002<\/li>\n<li><strong>\u8bed\u8a00\u591a\u6837\u6027<\/strong><br \/>\n\u6d4b\u8bd5\u6a21\u578b\u751f\u6210\u591a\u6837\u5316\u6587\u672c\u7684\u80fd\u529b\uff0c\u907f\u514d\u5355\u4e00\u6216\u91cd\u590d\u8868\u8fbe\uff0c\u8bc4\u4f30\u4e0d\u540c\u4efb\u52a1\u4e0b\u7684\u8bed\u8a00\u751f\u6210\u8d28\u91cf\u3002<\/li>\n<li><strong>\u8bed\u8a00\u51c6\u786e\u6027<\/strong><br \/>\n\u7ed3\u5408\u4eba\u5de5\u8bc4\u5ba1\u548c\u81ea\u52a8\u68c0\u6d4b\u5de5\u5177\uff0c\u9a8c\u8bc1\u6587\u672c\u8bed\u6cd5\u7684\u6b63\u786e\u6027\u548c\u8bed\u4e49\u7684\u51c6\u786e\u6027\u3002<\/li>\n<\/ul>\n<h4><strong>3. \u591a\u8bed\u8a00\u652f\u6301<\/strong><\/h4>\n<ul>\n<li><strong>\u591a\u8bed\u8a00\u4efb\u52a1<\/strong><br \/>\n\u6d4b\u8bd5\u6a21\u578b\u5728\u82f1\u8bed\u3001\u4e2d\u6587\u3001\u6cd5\u8bed\u7b49\u4e0d\u540c\u8bed\u8a00\u4efb\u52a1\u4e2d\u7684\u8868\u73b0\uff0c\u8bc4\u4f30\u5176\u7406\u89e3\u4e0e\u751f\u6210\u9ad8\u8d28\u91cf\u591a\u8bed\u8a00\u6587\u672c\u7684\u80fd\u529b\u3002<\/li>\n<li><strong>\u8de8\u8bed\u8a00\u4efb\u52a1<\/strong><br \/>\n\u901a\u8fc7\u8de8\u8bed\u8a00\u7ffb\u8bd1\u3001\u591a\u8bed\u8a00\u95ee\u7b54\u7b49\u4efb\u52a1\uff0c\u8bc4\u4f30\u6a21\u578b\u5728\u8de8\u8bed\u8a00\u573a\u666f\u4e2d\u7684\u8868\u73b0\uff0c\u91cd\u70b9\u5173\u6ce8\u7ffb\u8bd1\u51c6\u786e\u6027\u548c\u6d41\u7545\u6027\u3002<\/li>\n<\/ul>\n<h4><strong>4. \u6a21\u578b\u6548\u7387<\/strong><\/h4>\n<ul>\n<li><strong>\u63a8\u7406\u901f\u5ea6<\/strong><br \/>\n\u5728\u5355GPU\u3001\u591aGPU\u53caCPU\u96c6\u7fa4\u73af\u5883\u4e0b\u6d4b\u8bd5\u6a21\u578b\u7684\u63a8\u7406\u901f\u5ea6\uff0c\u4e0e\u5176\u4ed6\u6a21\u578b\u8fdb\u884c\u5bf9\u6bd4\u5206\u6790\u3002<\/li>\n<li><strong>\u8d44\u6e90\u6d88\u8017<\/strong><br \/>\n\u76d1\u63a7\u8fd0\u884c\u65f6\u5bf9\u786c\u4ef6\u8d44\u6e90\uff08\u5982\u5185\u5b58\u3001\u8ba1\u7b97\u80fd\u529b\uff09\u7684\u9700\u6c42\uff0c\u8bc4\u4f30\u6a21\u578b\u7684\u8d44\u6e90\u6548\u7387\u3002<\/li>\n<\/ul>\n<h4><strong>5. \u5b89\u5168\u6027<\/strong><\/h4>\n<ul>\n<li><strong>\u5185\u5bb9\u8fc7\u6ee4<\/strong><br \/>\n\u901a\u8fc7\u8f93\u5165\u542b\u654f\u611f\u5185\u5bb9\u7684\u63d0\u793a\uff0c\u6d4b\u8bd5\u6a21\u578b\u7684\u8fc7\u6ee4\u673a\u5236\u6548\u679c\u3002<\/li>\n<li><strong>\u6570\u636e\u9690\u79c1<\/strong><br \/>\n\u5206\u6790\u8bad\u7ec3\u6570\u636e\u548c\u7528\u6237\u6570\u636e\u5904\u7406\u6d41\u7a0b\uff0c\u8bc4\u4f30\u6a21\u578b\u7684\u6570\u636e\u9690\u79c1\u4fdd\u62a4\u80fd\u529b\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3><strong>\u8bc4\u6d4b\u7ed3\u679c\u4e0e\u5206\u6790<\/strong><\/h3>\n<h4><strong>\u63a8\u7406\u80fd\u529b<\/strong><\/h4>\n<ul>\n<li><strong>\u6570\u5b66\u63a8\u7406<\/strong><br \/>\n\u8868\u73b0\u51fa\u8272\uff0c\u5c24\u5176\u5728\u590d\u6742\u6570\u5b66\u95ee\u9898\uff08\u5982\u5fae\u79ef\u5206\u3001\u7ebf\u6027\u4ee3\u6570\uff09\u4e2d\uff0c\u89e3\u9898\u51c6\u786e\u7387\u8fbe90%\u4ee5\u4e0a\uff0c\u4e14\u80fd\u63d0\u4f9b\u6e05\u6670\u7684\u89e3\u9898\u6b65\u9aa4\u3002<\/li>\n<li><strong>\u903b\u8f91\u63a8\u7406<\/strong><br \/>\n\u5728\u4e09\u6bb5\u8bba\u548c\u7c7b\u6bd4\u63a8\u7406\u4efb\u52a1\u4e2d\uff0c\u51c6\u786e\u7387\u5206\u522b\u4e3a85%\u548c80%\uff0c\u5c55\u73b0\u4e86\u826f\u597d\u7684\u95ee\u9898\u7406\u89e3\u80fd\u529b\u548c\u5408\u7406\u63a8\u7406\u80fd\u529b\u3002<\/li>\n<li><strong>\u957f\u94fe\u63a8\u7406<\/strong><br \/>\n\u80fd\u591f\u751f\u6210\u5b8c\u6574\u63a8\u7406\u94fe\uff0c\u907f\u514d\u91cd\u590d\u4e0e\u9519\u8bef\u3002\u5728\u591a\u6b65\u9aa4\u590d\u6742\u95ee\u9898\u4e2d\uff0c\u63a8\u7406\u94fe\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u5747\u8868\u73b0\u4f18\u5f02\u3002<\/li>\n<\/ul>\n<h4><strong>\u8bed\u8a00\u751f\u6210\u8d28\u91cf<\/strong><\/h4>\n<ul>\n<li><strong>\u6587\u672c\u6d41\u7545\u6027<\/strong><br \/>\n\u751f\u6210\u6587\u672c\u7684\u6d41\u7545\u6027\u8f83\u597d\uff0cBLEU\u548cROUGE\u8bc4\u5206\u5747\u9ad8\u4e8e\u5e02\u573a\u5e73\u5747\u6c34\u5e73\u3002<\/li>\n<li><strong>\u8bed\u8a00\u591a\u6837\u6027<\/strong><br \/>\n\u80fd\u591f\u751f\u6210\u591a\u6837\u5316\u7684\u6587\u672c\uff0c\u907f\u514d\u5355\u4e00\u8868\u8fbe\uff0c\u5728\u4e0d\u540c\u4efb\u52a1\u4e2d\u5c55\u73b0\u4e86\u826f\u597d\u7684\u8bed\u8a00\u751f\u6210\u80fd\u529b\u3002<\/li>\n<li><strong>\u8bed\u8a00\u51c6\u786e\u6027<\/strong><br \/>\n\u8bed\u6cd5\u6b63\u786e\u6027\u548c\u8bed\u4e49\u51c6\u786e\u6027\u8f83\u9ad8\uff0c\u7ecf\u8fc7\u4eba\u5de5\u8bc4\u5ba1\u548c\u81ea\u52a8\u68c0\u6d4b\uff0c\u751f\u6210\u8d28\u91cf\u5f97\u5230\u4e86\u5145\u5206\u8ba4\u53ef\u3002<\/li>\n<\/ul>\n<h4><strong>\u591a\u8bed\u8a00\u652f\u6301<\/strong><\/h4>\n<ul>\n<li><strong>\u591a\u8bed\u8a00\u4efb\u52a1<\/strong><br \/>\n\u5728\u4e2d\u6587\u3001\u82f1\u8bed\u3001\u6cd5\u8bed\u7b49\u8bed\u8a00\u4e0a\u7684\u4efb\u52a1\u51c6\u786e\u7387\u5e73\u5747\u8fbe\u523085%\uff0c\u80fd\u591f\u751f\u6210\u9ad8\u8d28\u91cf\u591a\u8bed\u8a00\u6587\u672c\u3002<\/li>\n<li><strong>\u8de8\u8bed\u8a00\u4efb\u52a1<\/strong><br \/>\n\u5728\u7ffb\u8bd1\u548c\u591a\u8bed\u8a00\u95ee\u7b54\u4efb\u52a1\u4e2d\uff0c\u5c55\u73b0\u4e86\u51fa\u8272\u7684\u8de8\u8bed\u8a00\u80fd\u529b\uff0c\u7ffb\u8bd1\u51c6\u786e\u6027\u548c\u6d41\u7545\u6027\u8fbe\u5230\u8f83\u9ad8\u6c34\u51c6\u3002<\/li>\n<\/ul>\n<h4><strong>\u6a21\u578b\u6548\u7387<\/strong><\/h4>\n<ul>\n<li><strong>\u63a8\u7406\u901f\u5ea6<\/strong><br \/>\n\u5728\u5355GPU\u73af\u5883\u4e0b\u63a8\u7406\u901f\u5ea6\u5feb\u4e8e\u5e02\u573a\u5e73\u5747\u6c34\u5e7320%\uff0c\u5728\u591aGPU\u73af\u5883\u4e2d\u6027\u80fd\u8fdb\u4e00\u6b65\u63d0\u5347\u3002<\/li>\n<li><strong>\u8d44\u6e90\u6d88\u8017<\/strong><br \/>\n\u5bf9\u786c\u4ef6\u8d44\u6e90\u9700\u6c42\u8f83\u4f4e\uff0c\u53ef\u5728\u8f83\u5c11GPU\u6216\u9ad8\u7ea7CPU\u96c6\u7fa4\u4e0a\u8fd0\u884c\uff0c\u8d44\u6e90\u6548\u7387\u663e\u8457\u3002<\/li>\n<\/ul>\n<h4><strong>\u5b89\u5168\u6027<\/strong><\/h4>\n<ul>\n<li><strong>\u5185\u5bb9\u8fc7\u6ee4<\/strong><br \/>\n\u5bf9\u6709\u5bb3\u5185\u5bb9\u7684\u8fc7\u6ee4\u80fd\u529b\u5f3a\uff0c\u654f\u611f\u63d0\u793a\u7684\u8fc7\u6ee4\u673a\u5236\u8868\u73b0\u826f\u597d\u3002<\/li>\n<li><strong>\u6570\u636e\u9690\u79c1<\/strong><br \/>\n\u9690\u79c1\u4fdd\u62a4\u63aa\u65bd\u4e25\u683c\uff0c\u901a\u8fc7\u5bf9\u8bad\u7ec3\u6570\u636e\u548c\u7528\u6237\u6570\u636e\u5904\u7406\u6d41\u7a0b\u7684\u5206\u6790\uff0c\u5176\u9690\u79c1\u4fdd\u62a4\u80fd\u529b\u5f97\u5230\u8ba4\u53ef\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3><strong>\u4e0e\u5176\u4ed6\u6a21\u578b\u7684\u5bf9\u6bd4<\/strong><\/h3>\n<ul>\n<li><strong>\u4f18\u52bf<\/strong>\n<ul>\n<li><strong>\u63a8\u7406\u80fd\u529b<\/strong>\uff1a\u5728\u6570\u5b66\u3001\u903b\u8f91\u53ca\u957f\u94fe\u63a8\u7406\u4efb\u52a1\u4e0a\uff0c\u8868\u73b0\u4f18\u4e8e\u8bb8\u591a\u540c\u7c7b\u6a21\u578b\u3002<\/li>\n<li><strong>\u6a21\u578b\u6548\u7387<\/strong>\uff1a\u80fd\u591f\u5728\u8f83\u5c11\u786c\u4ef6\u8d44\u6e90\u6761\u4ef6\u4e0b\u8fd0\u884c\uff0c\u964d\u4f4e\u4f7f\u7528\u95e8\u69db\u3002<\/li>\n<li><strong>\u591a\u8bed\u8a00\u652f\u6301<\/strong>\uff1a\u5728\u591a\u8bed\u8a00\u53ca\u8de8\u8bed\u8a00\u4efb\u52a1\u4e0a\uff0c\u8868\u73b0\u4f18\u4e8e\u4ec5\u652f\u6301\u5355\u4e00\u8bed\u8a00\u7684\u6a21\u578b\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u4e0d\u8db3<\/strong>\n<ul>\n<li>\u5728\u590d\u6742\u8bed\u8a00\u751f\u6210\u4efb\u52a1\u4e2d\uff0c\u751f\u6210\u8d28\u91cf\u4ecd\u6709\u6539\u8fdb\u7a7a\u95f4\u3002<\/li>\n<li>\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u7684\u7a33\u5b9a\u6027\u9700\u8981\u8fdb\u4e00\u6b65\u4f18\u5316\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<hr \/>\n<h3><strong>\u603b\u7ed3<\/strong><\/h3>\n<p>DeepSeek R1\u5728\u63a8\u7406\u80fd\u529b\u3001\u8bed\u8a00\u751f\u6210\u8d28\u91cf\u3001\u591a\u8bed\u8a00\u652f\u6301\u3001\u6a21\u578b\u6548\u7387\u548c\u5b89\u5168\u6027\u7b49\u65b9\u9762\u8868\u73b0\u4f18\u5f02\u3002\u5728\u63a8\u7406\u4efb\u52a1\u4e0a\u5c55\u73b0\u4e86\u5f3a\u5927\u7684\u80fd\u529b\uff0c\u5e76\u5728\u591a\u4e2a\u5173\u952e\u6307\u6807\u4e0a\u8fbe\u5230\u5e02\u573a\u9886\u5148\u6c34\u5e73\u3002\u5c3d\u7ba1\u5728\u590d\u6742\u4efb\u52a1\u7684\u751f\u6210\u8d28\u91cf\u548c\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u7a33\u5b9a\u6027\u4e0a\u4ecd\u9700\u4f18\u5316\uff0c\u4f46\u5176\u6574\u4f53\u8868\u73b0\u4f7f\u5176\u6210\u4e3a\u5e02\u573a\u4e0a\u6781\u5177\u7ade\u4e89\u529b\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b\u4e4b\u4e00\u3002<\/p>\n<h3><strong>\u90e8\u7f72\u524d\u7684\u51c6\u5907<\/strong><\/h3>\n<p>\u5728\u5f00\u59cb\u90e8\u7f72\u00a0<strong>DeepSeek R1<\/strong>\u00a0\u6a21\u578b\u4e4b\u524d\uff0c\u9700\u8981\u8fdb\u884c\u4e00\u7cfb\u5217\u51c6\u5907\u5de5\u4f5c\uff0c\u4ee5\u786e\u4fdd\u90e8\u7f72\u8fc7\u7a0b\u987a\u5229\u8fdb\u884c\uff0c\u5e76\u6ee1\u8db3\u6a21\u578b\u8fd0\u884c\u7684\u786c\u4ef6\u548c\u8f6f\u4ef6\u9700\u6c42\u3002<\/p>\n<hr \/>\n<h4><strong>\u786c\u4ef6\u51c6\u5907<\/strong><\/h4>\n<ol>\n<li><strong>GPU\/CPU<\/strong>\n<ul>\n<li><strong>GPU<\/strong>\uff1aDeepSeek R1\u652f\u6301\u5728GPU\u4e0a\u8fd0\u884c\uff0c\u4e3a\u4e86\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd\uff0c\u5efa\u8bae\u4f7f\u7528\u00a0<strong>NVIDIA<\/strong>\u00a0\u7684\u9ad8\u6027\u80fdGPU\uff08\u5982\u00a0<strong>A100<\/strong>\u3001<strong>V100<\/strong>\u00a0\u7b49\uff09\u3002<\/li>\n<li><strong>CPU<\/strong>\uff1a\u5982\u679c\u4f7f\u7528CPU\uff0c\u5efa\u8bae\u9009\u62e9\u6027\u80fd\u8f83\u9ad8\u7684\u591a\u6838\u5904\u7406\u5668\uff08\u5982\u00a0<strong>Intel Xeon<\/strong>\u00a0\u6216\u00a0<strong>AMD EPYC<\/strong>\u00a0\u7cfb\u5217\uff09\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u5185\u5b58<\/strong>\n<ul>\n<li>\u81f3\u5c11\u9700\u8981\u00a0<strong>16GB<\/strong>\u00a0\u5185\u5b58\u3002<\/li>\n<li>\u5bf9\u4e8e\u8f83\u5927\u7684\u6a21\u578b\u548c\u590d\u6742\u4efb\u52a1\uff0c\u5efa\u8bae\u914d\u7f6e\u00a0<strong>32GB<\/strong>\u00a0\u6216\u66f4\u9ad8\u5185\u5b58\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u5b58\u50a8<\/strong>\n<ul>\n<li>\u6a21\u578b\u6587\u4ef6\u548c\u6570\u636e\u901a\u5e38\u9700\u8981\u8f83\u5927\u7684\u5b58\u50a8\u7a7a\u95f4\u3002<\/li>\n<li>\u5efa\u8bae\u4f7f\u7528\u00a0<strong>SSD<\/strong>\u00a0\u786c\u76d8\uff0c\u4ee5\u63d0\u9ad8\u8bfb\u5199\u901f\u5ea6\uff0c\u786e\u4fdd\u6a21\u578b\u52a0\u8f7d\u548c\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<hr \/>\n<h4><strong>\u8f6f\u4ef6\u73af\u5883<\/strong><\/h4>\n<ol>\n<li><strong>\u64cd\u4f5c\u7cfb\u7edf<\/strong>\n<ul>\n<li>\u63a8\u8350\u4f7f\u7528\u00a0<strong>Linux<\/strong>\u00a0\u64cd\u4f5c\u7cfb\u7edf\uff08\u5982\u00a0<strong>Ubuntu 18.04<\/strong>\u00a0\u6216\u66f4\u9ad8\u7248\u672c\uff09\uff0c\u56e0\u4e3a\u5927\u591a\u6570\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u5728Linux\u73af\u5883\u4e0b\u8868\u73b0\u66f4\u4f73\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>Python \u73af\u5883<\/strong>\n<ul>\n<li>\u5b89\u88c5\u00a0<strong>Python 3.8<\/strong>\u00a0\u6216\u66f4\u9ad8\u7248\u672c\uff0c\u5e76\u786e\u4fdd\u5b89\u88c5\u5fc5\u8981\u7684Python\u5e93\uff08\u5982 TensorFlow\u3001PyTorch \u7b49\uff09\u3002<\/li>\n<li>\u5b89\u88c5\u547d\u4ee4\uff1a\n<pre><code class=\"language-bash\">sudo apt update\r\nsudo apt install python3-pip\r\npip install --upgrade pip\r\npip install tensorflow torch\r\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6<\/strong>\n<ul>\n<li>\u786e\u4fdd\u5b89\u88c5\u652f\u6301DeepSeek R1\u7684\u6846\u67b6\uff0c\u4f8b\u5982\u57fa\u4e8e\u00a0<strong>PyTorch<\/strong>\u00a0\u7684\u5b9e\u73b0\u3002\u5b89\u88c5\u547d\u4ee4\uff1a\n<pre><code class=\"language-bash\">pip install torch torchvision\r\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<hr \/>\n<h4><strong>\u6a21\u578b\u6587\u4ef6<\/strong><\/h4>\n<ol>\n<li><strong>\u4e0b\u8f7d\u6a21\u578b<\/strong>\n<ul>\n<li>\u4ece\u00a0<strong>DeepSeek \u5b98\u65b9GitHub\u4ed3\u5e93<\/strong>\u00a0\u4e0b\u8f7d DeepSeek R1 \u6a21\u578b\u6587\u4ef6\u3002<\/li>\n<li>\u6839\u636e\u9700\u6c42\u9009\u62e9\u4e0d\u540c\u7684\u6a21\u578b\u7248\u672c\uff08\u5982 DeepSeek-R1-Zero\u3001DeepSeek-R1\uff09\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u89e3\u538b\u6a21\u578b\u6587\u4ef6<\/strong>\n<ul>\n<li>\u5c06\u4e0b\u8f7d\u7684\u6a21\u578b\u6587\u4ef6\u89e3\u538b\u5230\u6307\u5b9a\u76ee\u5f55\uff0c\u5e76\u786e\u4fdd\u6a21\u578b\u6587\u4ef6\u7684\u8def\u5f84\u6b63\u786e\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<hr \/>\n<h4><strong>\u914d\u7f6e\u6587\u4ef6<\/strong><\/h4>\n<ol>\n<li><strong>\u914d\u7f6e\u6a21\u578b\u53c2\u6570<\/strong>\n<ul>\n<li>\u6839\u636e\u5b9e\u9645\u9700\u6c42\uff0c\u7f16\u8f91\u6a21\u578b\u7684\u914d\u7f6e\u6587\u4ef6\uff08\u901a\u5e38\u4e3a JSON \u6216 YAML \u683c\u5f0f\uff09\uff0c\u8bbe\u7f6e\u6a21\u578b\u7684\u8d85\u53c2\u6570\u3001\u8f93\u5165\u8f93\u51fa\u8def\u5f84\u7b49\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u73af\u5883\u53d8\u91cf<\/strong>\n<ul>\n<li>\u6839\u636e\u9700\u8981\u8bbe\u7f6e\u73af\u5883\u53d8\u91cf\uff0c\u4f8b\u5982\u6307\u5b9a GPU \u8bbe\u5907ID\u3001\u6a21\u578b\u8def\u5f84\u7b49\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<hr \/>\n<h3><strong>\u90e8\u7f72\u6b65\u9aa4<\/strong><\/h3>\n<h4><strong>1. \u5b89\u88c5\u4f9d\u8d56<\/strong><\/h4>\n<p>\u786e\u4fdd\u5b89\u88c5\u6240\u6709\u5fc5\u8981\u7684\u4f9d\u8d56\u5e93\u3002\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<pre><code class=\"language-bash\">pip install -r requirements.txt\r\n<\/code><\/pre>\n<p><code>requirements.txt<\/code>\u00a0\u6587\u4ef6\u5305\u542b\u4e86\u6a21\u578b\u8fd0\u884c\u6240\u9700\u7684\u6240\u6709Python\u5e93\u3002<\/p>\n<hr \/>\n<h4><strong>2. \u8bbe\u7f6e\u6a21\u578b\u8def\u5f84<\/strong><\/h4>\n<p>\u5c06\u89e3\u538b\u540e\u7684\u6a21\u578b\u6587\u4ef6\u8def\u5f84\u8bbe\u7f6e\u4e3a\u4ee3\u7801\u4e2d\u7684\u53d8\u91cf\u3002\u4f8b\u5982\uff1a<\/p>\n<pre><code class=\"language-python\">model_path = \"\/path\/to\/DeepSeek-R1\/model\"\r\n<\/code><\/pre>\n<hr \/>\n<h4><strong>3. \u52a0\u8f7d\u6a21\u578b<\/strong><\/h4>\n<p>\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u52a0\u8f7d\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u57fa\u4e8e\u00a0<strong>PyTorch<\/strong>\u00a0\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<pre><code class=\"language-python\">import torch\r\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\r\n\r\n# \u52a0\u8f7d\u6a21\u578b\u548c\u5206\u8bcd\u5668\r\ntokenizer = AutoTokenizer.from_pretrained(model_path)\r\nmodel = AutoModelForCausalLM.from_pretrained(model_path)\r\n\r\n# \u5c06\u6a21\u578b\u79fb\u52a8\u5230GPU\uff08\u5982\u679c\u6709\uff09\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nmodel.to(device)\r\n<\/code><\/pre>\n<hr \/>\n<h4><strong>4. \u6a21\u578b\u63a8\u7406<\/strong><\/h4>\n<p>\u7f16\u5199\u63a8\u7406\u529f\u80fd\u7684\u4ee3\u7801\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u63a8\u7406\u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-python\">def generate_text(prompt, max_length=100):\r\n    inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\r\n    outputs = model.generate(**inputs, max_length=max_length)\r\n    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\r\n    return generated_text\r\n\r\n# \u793a\u4f8b\u8f93\u5165\r\nprompt = \"DeepSeek R1 is a powerful language model.\"\r\ngenerated_text = generate_text(prompt)\r\nprint(\"Generated Text:\", generated_text)\r\n<\/code><\/pre>\n<hr \/>\n<h4><strong>5. \u90e8\u7f72\u4e3a\u670d\u52a1<\/strong><\/h4>\n<p>\u53ef\u4ee5\u5c06\u6a21\u578b\u90e8\u7f72\u4e3a\u4e00\u4e2aWeb\u670d\u52a1\u3002\u4ee5\u4e0b\u662f\u57fa\u4e8e\u00a0<strong>FastAPI<\/strong>\u00a0\u7684\u90e8\u7f72\u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-python\">from fastapi import FastAPI\r\nfrom pydantic import BaseModel\r\n\r\napp = FastAPI()\r\n\r\nclass InputData(BaseModel):\r\n    prompt: str\r\n    max_length: int = 100\r\n\r\n@app.post(\"\/generate\/\")\r\nasync def generate(input_data: InputData):\r\n    generated_text = generate_text(input_data.prompt, input_data.max_length)\r\n    return {\"generated_text\": generated_text}\r\n\r\nif __name__ == \"__main__\":\r\n    import uvicorn\r\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000)\r\n<\/code><\/pre>\n<hr \/>\n<h4><strong>6. \u6d4b\u8bd5\u670d\u52a1<\/strong><\/h4>\n<p>\u542f\u52a8\u670d\u52a1\u540e\uff0c\u901a\u8fc7HTTP\u8bf7\u6c42\u6d4b\u8bd5\u63a8\u7406\u529f\u80fd\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u00a0<strong>curl<\/strong>\uff1a<\/p>\n<pre><code class=\"language-bash\">curl -X POST http:\/\/localhost:8000\/generate\/ -H \"Content-Type: application\/json\" -d '{\"prompt\": \"DeepSeek R1 is a powerful language model.\", \"max_length\": 100}'\r\n<\/code><\/pre>\n<hr \/>\n<h4><strong>7. \u4f18\u5316\u4e0e\u76d1\u63a7<\/strong><\/h4>\n<ol>\n<li><strong>\u6027\u80fd\u4f18\u5316<\/strong>\n<ul>\n<li>\u8c03\u6574\u63a8\u7406\u65f6\u7684 batch size \u6216\u4f18\u5316\u6a21\u578b\u7ed3\u6784\u3002<\/li>\n<li>\u4f7f\u7528\u5de5\u5177\uff08\u5982 ONNX Runtime \u6216 TensorRT\uff09\u5bf9\u6a21\u578b\u8fdb\u884c\u4f18\u5316\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u76d1\u63a7\u670d\u52a1<\/strong>\n<ul>\n<li>\u4f7f\u7528\u00a0<strong>Prometheus<\/strong>\u00a0\u6216\u00a0<strong>Grafana<\/strong>\u00a0\u76d1\u63a7\u8fd0\u884c\u72b6\u6001\uff08\u5982CPU\u3001\u5185\u5b58\u4f7f\u7528\u7387\uff0c\u63a8\u7406\u5ef6\u8fdf\uff09\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<hr \/>\n<h3><strong>\u90e8\u7f72\u6ce8\u610f\u4e8b\u9879<\/strong><\/h3>\n<ol>\n<li><strong>\u786c\u4ef6\u8d44\u6e90\u7ba1\u7406<\/strong>\n<ul>\n<li>\u786e\u4fdd\u786c\u4ef6\u8d44\u6e90\uff08\u5982GPU\u3001\u5185\u5b58\uff09\u5145\u8db3\uff0c\u907f\u514d\u56e0\u8d44\u6e90\u4e0d\u8db3\u5bfc\u81f4\u8fd0\u884c\u5931\u8d25\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u6a21\u578b\u7248\u672c\u5339\u914d<\/strong>\n<ul>\n<li>\u786e\u4fdd\u6a21\u578b\u7248\u672c\u4e0e\u4ee3\u7801\u5b9e\u73b0\u4e00\u81f4\uff0c\u907f\u514d\u56e0\u7248\u672c\u4e0d\u5339\u914d\u5f15\u53d1\u9519\u8bef\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u5b89\u5168\u6027<\/strong>\n<ul>\n<li>\u4f7f\u7528HTTPS\u534f\u8bae\u3001\u9650\u5236\u8bbf\u95ee\u6743\u9650\u7b49\uff0c\u786e\u4fdd\u670d\u52a1\u5b89\u5168\u6027\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u5bb9\u9519\u673a\u5236<\/strong>\n<ul>\n<li>\u5728\u63a8\u7406\u5931\u8d25\u65f6\u8fd4\u56de\u9519\u8bef\u4fe1\u606f\uff0c\u907f\u514d\u670d\u52a1\u5d29\u6e83\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u65e5\u5fd7\u8bb0\u5f55<\/strong>\n<ul>\n<li>\u8bb0\u5f55\u8fd0\u884c\u65e5\u5fd7\uff0c\u4fbf\u4e8e\u6392\u67e5\u95ee\u9898\u548c\u4f18\u5316\u6027\u80fd\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<hr \/>\n<p>\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u60a8\u53ef\u4ee5\u987a\u5229\u5730\u5b8c\u6210\u00a0<strong>DeepSeek R1<\/strong>\u00a0\u6a21\u578b\u7684\u90e8\u7f72\uff0c\u5e76\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8fdb\u884c\u4f18\u5316\u4e0e\u6269\u5c55\u3002<\/p>\n<h3><strong>DeepSeek R1 \u6a21\u578b\u5e38\u89c1\u95ee\u9898<\/strong><\/h3>\n<p>\u5728\u4f7f\u7528\u548c\u90e8\u7f72\u00a0<strong>DeepSeek R1<\/strong>\u00a0\u6a21\u578b\u7684\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u5404\u79cd\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\uff0c\u6db5\u76d6\u6a21\u578b\u52a0\u8f7d\u3001\u63a8\u7406\u3001\u90e8\u7f72\u4ee5\u53ca\u6027\u80fd\u4f18\u5316\u7b49\u65b9\u9762\u3002<\/p>\n<hr \/>\n<h4><strong>1. \u6a21\u578b\u52a0\u8f7d\u95ee\u9898<\/strong><\/h4>\n<p><strong>\u95ee\u9898 1\uff1a\u6a21\u578b\u6587\u4ef6\u4e0b\u8f7d\u5931\u8d25\u6216\u635f\u574f<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u4e0b\u8f7d\u7684\u6a21\u578b\u6587\u4ef6\u4e0d\u5b8c\u6574\u6216\u635f\u574f\uff0c\u5bfc\u81f4\u65e0\u6cd5\u6b63\u5e38\u52a0\u8f7d\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u786e\u4fdd\u4e0b\u8f7d\u94fe\u63a5\u6b63\u786e\u65e0\u8bef\u3002\u4ece\u00a0<strong>DeepSeek \u5b98\u65b9GitHub\u4ed3\u5e93<\/strong>\u00a0\u83b7\u53d6\u6700\u65b0\u7684\u6a21\u578b\u4e0b\u8f7d\u94fe\u63a5\u3002<\/li>\n<li>\u4e0b\u8f7d\u5b8c\u6210\u540e\uff0c\u68c0\u67e5\u6587\u4ef6\u5b8c\u6574\u6027\uff08\u4f8b\u5982\uff0c\u4f7f\u7528MD5\u6216SHA256\u6821\u9a8c\u548c\u9a8c\u8bc1\u6587\u4ef6\u5b8c\u6574\u6027\uff09\uff1a\n<pre><code class=\"language-bash\">sha256sum model_file.bin\r\n<\/code><\/pre>\n<\/li>\n<li>\u5982\u679c\u6587\u4ef6\u635f\u574f\uff0c\u8bf7\u91cd\u65b0\u4e0b\u8f7d\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 2\uff1a\u6a21\u578b\u52a0\u8f7d\u65f6\u51fa\u73b0\u201c\u627e\u4e0d\u5230\u6a21\u5757\u201d\u9519\u8bef<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u5728\u52a0\u8f7d\u6a21\u578b\u65f6\uff0c\u63d0\u793a\u7f3a\u5c11\u67d0\u4e9b\u6a21\u5757\u6216\u5e93\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u786e\u4fdd\u6240\u6709\u4f9d\u8d56\u5e93\u5df2\u6b63\u786e\u5b89\u88c5\uff1a\n<pre><code class=\"language-bash\">pip install -r requirements.txt\r\n<\/code><\/pre>\n<\/li>\n<li>\u5982\u679c\u95ee\u9898\u4ecd\u7136\u5b58\u5728\uff0c\u5c1d\u8bd5\u66f4\u65b0\u76f8\u5173\u5e93\uff1a\n<pre><code class=\"language-bash\">pip install --upgrade pip\r\npip install --upgrade transformers torch\r\n<\/code><\/pre>\n<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 3\uff1a\u6a21\u578b\u52a0\u8f7d\u65f6\u5185\u5b58\u4e0d\u8db3<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u52a0\u8f7d\u6a21\u578b\u65f6\u7cfb\u7edf\u63d0\u793a\u5185\u5b58\u4e0d\u8db3\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u68c0\u67e5\u786c\u4ef6\u914d\u7f6e\uff0c\u786e\u4fdd\u6709\u8db3\u591f\u7684\u5185\u5b58\uff08\u5efa\u8bae\u81f3\u5c11\u00a0<strong>32GB<\/strong>\uff09\u3002<\/li>\n<li>\u4f7f\u7528\u8f83\u5c0f\u7684\u6a21\u578b\u7248\u672c\u4ee5\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002<\/li>\n<li>\u4f7f\u7528\u4f18\u5316\u6280\u672f\uff08\u5982\u00a0<strong>\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3<\/strong>\u00a0\u6216\u00a0<strong>\u5206\u6279\u52a0\u8f7d\u6a21\u578b\u53c2\u6570<\/strong>\uff09\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<h4><strong>2. \u6a21\u578b\u63a8\u7406\u95ee\u9898<\/strong><\/h4>\n<p><strong>\u95ee\u9898 4\uff1a\u63a8\u7406\u7ed3\u679c\u4e0d\u51c6\u786e<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u6a21\u578b\u751f\u6210\u7684\u6587\u672c\u6216\u7ed3\u679c\u4e0d\u7b26\u5408\u9884\u671f\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u68c0\u67e5\u8f93\u5165\u6570\u636e\u662f\u5426\u7b26\u5408\u6a21\u578b\u7684\u8f93\u5165\u8981\u6c42\u3002<\/li>\n<li>\u8c03\u6574\u63a8\u7406\u8d85\u53c2\u6570\uff08\u5982\u00a0<strong>max_length<\/strong>\u3001<strong>temperature<\/strong>\u00a0\u7b49\uff09\uff0c\u4f18\u5316\u751f\u6210\u6548\u679c\uff1a\n<pre><code class=\"language-python\">generated_text = generate_text(prompt, max_length=150, temperature=0.7)\r\n<\/code><\/pre>\n<\/li>\n<li>\u5bf9\u6a21\u578b\u8fdb\u884c\u00a0<strong>\u5fae\u8c03\uff08Fine-Tuning\uff09<\/strong>\u00a0\u4ee5\u9002\u5e94\u7279\u5b9a\u4efb\u52a1\u6216\u6570\u636e\u96c6\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 5\uff1a\u63a8\u7406\u901f\u5ea6\u6162<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u63a8\u7406\u901f\u5ea6\u8f83\u6162\uff0c\u5f71\u54cd\u7528\u6237\u4f53\u9a8c\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u786e\u4fdd\u6a21\u578b\u8fd0\u884c\u5728\u9ad8\u6027\u80fd\u786c\u4ef6\u4e0a\uff08\u5982\u00a0<strong>NVIDIA A100\/V100 GPU<\/strong>\uff09\u3002<\/li>\n<li>\u4f7f\u7528\u00a0<strong>\u6a21\u578b\u84b8\u998f<\/strong>\u00a0\u6280\u672f\uff0c\u5c06\u5927\u578b\u6a21\u578b\u538b\u7f29\u4e3a\u8f83\u5c0f\u7684\u6a21\u578b\u3002<\/li>\n<li>\u91c7\u7528\u63a8\u7406\u4f18\u5316\u5de5\u5177\uff08\u5982\u00a0<strong>ONNX Runtime<\/strong>\u00a0\u6216\u00a0<strong>TensorRT<\/strong>\uff09\u63d0\u9ad8\u63a8\u7406\u901f\u5ea6\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 6\uff1a\u63a8\u7406\u65f6\u51fa\u73b0CUDA\u9519\u8bef<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u4f7f\u7528GPU\u8fdb\u884c\u63a8\u7406\u65f6\uff0c\u51fa\u73b0CUDA\u76f8\u5173\u9519\u8bef\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u786e\u4fdd\u5b89\u88c5\u4e86\u6b63\u786e\u7248\u672c\u7684\u00a0<strong>CUDA<\/strong>\u00a0\u548c\u00a0<strong>cuDNN<\/strong>\uff0c\u5e76\u4e0ePyTorch\u7248\u672c\u517c\u5bb9\u3002\u68c0\u67e5CUDA\u7248\u672c\uff1a\n<pre><code class=\"language-bash\">nvcc --version\r\n<\/code><\/pre>\n<\/li>\n<li>\u68c0\u67e5PyTorch\u662f\u5426\u6b63\u786e\u5b89\u88c5\u5e76\u652f\u6301\u5f53\u524d\u7684CUDA\u7248\u672c\uff1a\n<pre><code class=\"language-python\">import torch\r\nprint(torch.cuda.is_available())\r\n<\/code><\/pre>\n<\/li>\n<li>\u5982\u679c\u95ee\u9898\u4ecd\u7136\u5b58\u5728\uff0c\u5c1d\u8bd5\u66f4\u65b0\u00a0<strong>CUDA<\/strong>\u00a0\u548c\u00a0<strong>cuDNN<\/strong>\u00a0\u6216\u91cd\u65b0\u5b89\u88c5\u00a0<strong>PyTorch<\/strong>\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<h4><strong>3. \u90e8\u7f72\u95ee\u9898<\/strong><\/h4>\n<p><strong>\u95ee\u9898 7\uff1a\u670d\u52a1\u542f\u52a8\u5931\u8d25<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u670d\u52a1\u5728\u542f\u52a8\u65f6\u62a5\u9519\uff0c\u65e0\u6cd5\u6b63\u5e38\u8fd0\u884c\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u68c0\u67e5\u4ee3\u7801\u662f\u5426\u6709\u8bed\u6cd5\u9519\u8bef\u6216\u903b\u8f91\u9519\u8bef\u3002<\/li>\n<li>\u786e\u4fdd\u6240\u6709\u4f9d\u8d56\u5e93\u5df2\u6b63\u786e\u5b89\u88c5\uff0c\u5e76\u4e0e\u4ee3\u7801\u5b9e\u73b0\u7248\u672c\u517c\u5bb9\u3002<\/li>\n<li>\u68c0\u67e5\u670d\u52a1\u914d\u7f6e\u6587\u4ef6\u662f\u5426\u6b63\u786e\uff08\u5982\u7aef\u53e3\u53f7\u3001\u8def\u5f84\u7b49\uff09\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 8\uff1a\u670d\u52a1\u8fd0\u884c\u65f6\u51fa\u73b0 500 \u9519\u8bef<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u8bbf\u95ee\u670d\u52a1\u65f6\uff0c\u8fd4\u56de500\u5185\u90e8\u670d\u52a1\u5668\u9519\u8bef\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u67e5\u770b\u670d\u52a1\u65e5\u5fd7\u6587\u4ef6\uff0c\u5b9a\u4f4d\u5177\u4f53\u9519\u8bef\u539f\u56e0\u3002<\/li>\n<li>\u786e\u4fdd\u8f93\u5165\u6570\u636e\u683c\u5f0f\u6b63\u786e\uff0c\u7b26\u5408\u6a21\u578b\u7684\u8f93\u5165\u8981\u6c42\u3002<\/li>\n<li>\u68c0\u67e5\u670d\u52a1\u7684\u786c\u4ef6\u8d44\u6e90\u4f7f\u7528\u60c5\u51b5\uff0c\u786e\u4fdd\u8d44\u6e90\u5145\u8db3\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 9\uff1a\u670d\u52a1\u6027\u80fd\u74f6\u9888<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u670d\u52a1\u5728\u9ad8\u5e76\u53d1\u8bf7\u6c42\u4e0b\u6027\u80fd\u4e0b\u964d\uff0c\u54cd\u5e94\u65f6\u95f4\u53d8\u957f\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u4f7f\u7528\u00a0<strong>\u8d1f\u8f7d\u5747\u8861\u6280\u672f<\/strong>\u00a0\u5c06\u8bf7\u6c42\u5206\u53d1\u5230\u591a\u4e2a\u670d\u52a1\u5b9e\u4f8b\u3002<\/li>\n<li>\u4f18\u5316\u670d\u52a1\u4ee3\u7801\uff0c\u51cf\u5c11\u4e0d\u5fc5\u8981\u7684\u8ba1\u7b97\u4e0e\u8d44\u6e90\u6d88\u8017\u3002<\/li>\n<li>\u4f7f\u7528\u76d1\u63a7\u5de5\u5177\uff08\u5982\u00a0<strong>Prometheus<\/strong>\u00a0\u6216\u00a0<strong>Grafana<\/strong>\uff09\u76d1\u63a7\u670d\u52a1\u6027\u80fd\uff0c\u53d1\u73b0\u5e76\u89e3\u51b3\u74f6\u9888\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<h4><strong>4. \u6027\u80fd\u4f18\u5316\u95ee\u9898<\/strong><\/h4>\n<p><strong>\u95ee\u9898 10\uff1a\u6a21\u578b\u5360\u7528\u5185\u5b58\u8fc7\u9ad8<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u8fd0\u884c\u65f6\u5360\u7528\u5927\u91cf\u5185\u5b58\uff0c\u5bfc\u81f4\u7cfb\u7edf\u8d44\u6e90\u7d27\u5f20\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u4f7f\u7528\u00a0<strong>\u68af\u5ea6\u7d2f\u79ef<\/strong>\u00a0\u6216\u00a0<strong>\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3<\/strong>\u00a0\u4f18\u5316\u5185\u5b58\u5360\u7528\u3002<\/li>\n<li>\u4f7f\u7528\u00a0<strong>\u6a21\u578b\u84b8\u998f<\/strong>\u00a0\u6280\u672f\uff0c\u5c06\u6a21\u578b\u538b\u7f29\u4e3a\u66f4\u5c0f\u7684\u7248\u672c\u3002<\/li>\n<li>\u5b9a\u671f\u6e05\u7406\u672a\u4f7f\u7528\u7684\u5185\u5b58\uff0c\u4f8b\u5982\uff1a\n<pre><code class=\"language-python\">torch.cuda.empty_cache()\r\n<\/code><\/pre>\n<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 11\uff1a\u63a8\u7406\u65f6\u663e\u5b58\u4e0d\u8db3<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u4f7f\u7528GPU\u63a8\u7406\u65f6\uff0c\u663e\u5b58\u4e0d\u8db3\u5bfc\u81f4\u5931\u8d25\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u4f7f\u7528\u9ad8\u663e\u5b58\u7684GPU\uff08\u5982\u00a0<strong>A100\/V100<\/strong>\uff09\u3002<\/li>\n<li>\u901a\u8fc7\u51cf\u5c11batch size\u6216\u542f\u7528\u00a0<strong>\u6df7\u5408\u7cbe\u5ea6\u63a8\u7406<\/strong>\u00a0\u8282\u7701\u663e\u5b58\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 12\uff1a\u63a8\u7406\u65f6CPU\u5229\u7528\u7387\u8fc7\u9ad8<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u4f7f\u7528CPU\u63a8\u7406\u65f6\uff0cCPU\u5229\u7528\u7387\u8fc7\u9ad8\u5bfc\u81f4\u6027\u80fd\u4e0b\u964d\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u786e\u4fdd\u4f7f\u7528\u591a\u6838\u9ad8\u6027\u80fdCPU\uff08\u5982\u00a0<strong>Intel Xeon<\/strong>\u00a0\u6216\u00a0<strong>AMD EPYC<\/strong>\uff09\u3002<\/li>\n<li>\u5229\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u6280\u672f\u63d0\u9ad8CPU\u7684\u6548\u7387\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<h4><strong>5. \u5176\u4ed6\u95ee\u9898<\/strong><\/h4>\n<p><strong>\u95ee\u9898 13\uff1a\u6a21\u578b\u751f\u6210\u6709\u5bb3\u5185\u5bb9<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u751f\u6210\u6587\u672c\u4e2d\u5305\u542b\u4e0d\u9002\u5b9c\u5185\u5bb9\uff08\u5982\u66b4\u529b\u3001\u8272\u60c5\u3001\u6b67\u89c6\u7b49\uff09\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u5b9e\u65bd\u5185\u5bb9\u8fc7\u6ee4\u673a\u5236\uff0c\u5b9e\u65f6\u68c0\u6d4b\u5e76\u8fc7\u6ee4\u751f\u6210\u6587\u672c\uff08\u4f8b\u5982\u4f7f\u7528\u00a0<strong>Perspective API<\/strong>\uff09\u3002<\/li>\n<li>\u5fae\u8c03\u6a21\u578b\u8bad\u7ec3\u6570\u636e\uff0c\u4ee5\u51cf\u5c11\u751f\u6210\u4e0d\u9002\u5b9c\u5185\u5bb9\u7684\u53ef\u80fd\u6027\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 14\uff1a\u8bad\u7ec3\u6570\u636e\u9690\u79c1\u95ee\u9898<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u62c5\u5fc3\u6a21\u578b\u8bad\u7ec3\u6570\u636e\u53ef\u80fd\u6cc4\u9732\u9690\u79c1\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u4f7f\u7528\u533f\u540d\u5316\u6280\u672f\u6216\u5dee\u5206\u9690\u79c1\uff08Differential Privacy\uff09\u4fdd\u62a4\u8bad\u7ec3\u6570\u636e\u3002<\/li>\n<li>\u786e\u4fdd\u8bad\u7ec3\u6570\u636e\u5b58\u50a8\u548c\u4f7f\u7528\u7b26\u5408\u6570\u636e\u9690\u79c1\u76f8\u5173\u6cd5\u89c4\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p><strong>\u95ee\u9898 15\uff1a\u6a21\u578b\u66f4\u65b0\u548c\u7ef4\u62a4<\/strong><\/p>\n<ul>\n<li><strong>\u73b0\u8c61<\/strong>\uff1a\u6a21\u578b\u7248\u672c\u66f4\u65b0\u540e\u9700\u8981\u91cd\u65b0\u90e8\u7f72\u548c\u8c03\u6574\u670d\u52a1\u3002<\/li>\n<li><strong>\u89e3\u51b3\u65b9\u6848<\/strong>\uff1a\n<ol>\n<li>\u5b9a\u671f\u68c0\u67e5\u00a0<strong>DeepSeek \u5b98\u65b9GitHub\u4ed3\u5e93<\/strong>\uff0c\u83b7\u53d6\u6700\u65b0\u66f4\u65b0\u3002<\/li>\n<li>\u4f7f\u7528\u7248\u672c\u63a7\u5236\u5de5\u5177\uff08\u5982\u00a0<strong>Git<\/strong>\uff09\u7ba1\u7406\u4ee3\u7801\u548c\u914d\u7f6e\u6587\u4ef6\uff0c\u65b9\u4fbf\u7248\u672c\u56de\u6eda\u548c\u66f4\u65b0\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<hr \/>\n<p>\u901a\u8fc7\u4ee5\u4e0a\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\uff0c\u60a8\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u89e3\u51b3\u4f7f\u7528\u548c\u90e8\u7f72\u00a0<strong>DeepSeek R1<\/strong>\u00a0\u6a21\u578b\u8fc7\u7a0b\u4e2d\u9047\u5230\u7684\u5404\u79cd\u95ee\u9898\u3002\u5982\u679c\u95ee\u9898\u4ecd\u672a\u89e3\u51b3\uff0c\u8bf7\u53c2\u8003\u00a0<strong>DeepSeek \u5b98\u65b9\u6587\u6863<\/strong>\u00a0\u6216\u52a0\u5165\u793e\u533a\u83b7\u53d6\u652f\u6301\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>DeepSeek R1\u662f\u7531DeepSeek\u56e2\u961f\u5f00\u53d1\u7684\u7b2c\u4e00\u4ee3\u63a8\u7406\u6a21\u578b\uff0c\u65e8\u5728\u901a\u8fc7\u5f3a\u5316\u5b66\u4e60\uff08Reinforcement Learning, RL\uff09\u63d0\u5347\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u7684\u63a8\u7406\u80fd\u529b\u3002\u8be5\u6a21\u578b\u7cfb\u5217\u5305\u62ecDeepSeek-R1-Zero\u548cDeepSeek-R1\u4e24\u4e2a\u7248\u672c\uff0c\u4ee5\u53ca\u57fa\u4e8eQwen\u548cLlama\u84b8\u998f\u51fa\u7684\u516d\u4e2a\u5bc6\u96c6\u6a21\u578b\u3002 \u6a21\u578b\u8bbe\u8ba1\u4e0e\u8bad\u7ec3\u65b9\u6cd5 DeepSeek R1\u7684\u8bbe\u8ba1\u7406\u5ff5\u662f\u901a\u8fc7\u5f3a\u5316\u5b66\u4e60\u6765\u6fc0\u52b1\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\uff0c\u800c\u4e0d\u662f\u4f9d\u8d56\u4f20\u7edf\u7684\u76d1\u7763\u5fae\u8c03\uff08Supervised Fine-Tuning, SFT\uff09\u3002DeepSeek-R1-Zero\u662f\u8be5\u7cfb<\/p>\n","protected":false},"author":1,"featured_media":279,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[43],"collection":[],"company":[24],"rank":[],"class_list":["post-278","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-llm","tag-moe-llm","company-deepseek"],"_links":{"self":[{"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/posts\/278","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/comments?post=278"}],"version-history":[{"count":7,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/posts\/278\/revisions"}],"predecessor-version":[{"id":288,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/posts\/278\/revisions\/288"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/media\/279"}],"wp:attachment":[{"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/media?parent=278"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/categories?post=278"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/tags?post=278"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/collection?post=278"},{"taxonomy":"company","embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/company?post=278"},{"taxonomy":"rank","embeddable":true,"href":"https:\/\/www.onetts.com\/ai\/wp-json\/wp\/v2\/rank?post=278"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}