{"id":26363,"date":"2025-02-22T15:12:52","date_gmt":"2025-02-22T07:12:52","guid":{"rendered":"https:\/\/www.aisharenet.com\/?p=26363"},"modified":"2025-02-22T15:12:52","modified_gmt":"2025-02-22T07:12:52","slug":"jiaohuiputongrenzhengai","status":"publish","type":"post","link":"https:\/\/www.kdjingpai.com\/en\/jiaohuiputongrenzhengai\/","title":{"rendered":"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528 DeepSeek-R1 \u7684\u6559\u7a0b"},"content":{"rendered":"

DeepSeek-R1<\/a> \u5bf9\u6bd4\u5176\u4ed6\u5927\u6a21\u578b\u6ca1\u4ec0\u4e48\u7279\u522b\uff0c\u4f60\u7684\u60ca\u559c\u5728\u4e8e\u770b\u5230\u601d\u8003\u8fc7\u7a0b\u6216\u4f18\u79c0\u7684\u4e2d\u6587\u8868\u8fbe\u80fd\u529b\u3002\u5982\u679c\u4f60\u4f7f\u7528\u8fc7 ChatGPT<\/a> \u89c9\u5f97\u7d22\u7136\u65e0\u5473\uff0c\u90a3\u4e48 DeepSeek-R1 \u5e26\u6765\u7684\u60ca\u559c\u53ef\u80fd\u662f\u9519\u89c9\u3002\u5982\u679c\u4f60\u6bcf\u5929\u5fd9\u7740\u5e26\u5b69\u5b50\u3001\u9001\u5916\u5356\uff0c\u6839\u672c\u6ca1\u5fc5\u8981\u5173\u6ce8DeepSeek\uff0c\u9664\u4e86\u6d6a\u8d39\u65f6\u95f4\u4ec0\u4e48\u4e5f\u5f97\u4e0d\u5230\u3002<\/p><\/blockquote>\n

\u80cc\u666f<\/h2>\n

\u5173\u4e8e DeepSeek-R1 \u7684\u91cd\u8981\u80cc\u666f\u4fe1\u606f\uff0c\u5982\u679c\u4f60\u4e0d\u516b\u5366\uff0c\u53ef\u4ee5\u8df3\u8fc7\u3002DeepSeek-R1\u8bde\u751f\u4e8e\u4e00\u5bb6\u77e5\u540d\u91cf\u5316\u6295\u8d44\u516c\u53f8\u2014\u2014\u201c\u5e7b\u65b9\u91cf\u5316\u201d\uff0c\u516c\u53f8\u5168\u540d\u201c\u676d\u5dde\u6df1\u5ea6\u6c42\u7d22\u4eba\u5de5\u667a\u80fd\u57fa\u7840\u6280\u672f\u7814\u7a76\u6709\u9650\u516c\u53f8\u201d\uff0c\u521b\u59cb\u4eba\u6881\u6587\u950b\u3002<\/p>\n

DeepSeek \u5728 2024\u5e7411\u670820\u65e5\u5c31\u53d1\u5e03\u4e86\u63a8\u7406\u6a21\u578b\u201cDeepSeek-R1-Lite\u201d<\/a>\uff0c\u53ef\u4ee5\u5728\u7528\u6237\u754c\u9762\u5f00\u542f\u201c\u6df1\u5ea6\u601d\u8003\u201d\u4f7f\u7528\u3002\u4f46\u662f DeepSeek-R1-Lite \u4f7f\u7528\u4e00\u4e2a\u8f83\u5c0f\u7684\u57fa\u5ea7\u6a21\u578b\u8bad\u7ec3\uff0c\u5f97\u5230\u7684\u5173\u6ce8\u8f83\u5c11\uff0c\u800c DeepSeek-R1 \u4f7f\u7528\u4e86\u8f83\u5927\u7684\u57fa\u5ea7\u6a21\u578b\u201cDeepSeek-V3-Base\u201d\u8bad\u7ec3\u800c\u6765\uff0c\u6574\u4f53\u80fd\u529b\u5f97\u5230\u4e86\u5f88\u5927\u63d0\u5347\u3002\u56e0\u6b64 DeepSeek-R1-Lite \u5c31\u662f DeepSeek-R1 2\u4e2a\u6708\u524d\u7684\u9884\u89c8\u7248\u3002\u8fd9\u4e48\u7740\u6025\u516c\u5e03\u7ed9\u7528\u6237\u4f53\u9a8c…<\/p>\n

\u57281\u670820\u65e5\u6881\u6587\u950b\u53c2\u52a0\u91cd\u8981\u201c\u4f1a\u8bae\u201d\u5e76\u53d1\u8a00\uff0c\u540c\u65e5\u53d1\u5e03 DeepSeek-R1 \u6280\u672f\u62a5\u544a<\/a>\uff0c\u8fd9\u79cd\u5de7\u5408\u597d\u50cf\u54ea\u5412\u7206\u706b\u4e00\u6837\u80cc\u540e\u4e0d\u77e5\u9053\u6709\u4ec0\u4e48\u6545\u4e8b…<\/p>\n

DeepSeek-R1 \u524d\u671f\u706b\u7206\u7684\u539f\u56e0\u4e3b\u8981\u5728\u6280\u672f\u9886\u57df\uff0c\u56e0\u4e3a\u4ed6\u5f00\u653e\u4e86\u6280\u672f\u62a5\u544a\uff0c\u544a\u8bc9\u5927\u5bb6\u5982\u4f55\u7528\u4f4e\u6210\u672c\uff08557.6\u4e07\u7f8e\u5143\u7684GPU\u6210\u672c\uff0c\u8fd9\u91cc\u6709\u4e0d\u540c\u7684\u770b\u6cd5<\/a>\uff09\u590d\u73b0”o1<\/a>“\u7684\uff0c\u5e76\u4e14\u4ed6\u7684\u5e76\u53d1\u80fd\u529b\u5f88\u9ad8\uff0c\u7528\u66f4\u5c11\u7684\u786c\u4ef6\u8d44\u6e90\u65e2\u53ef\u8fd0\u884c\u8d85\u5927\u89c4\u6a21\u7684\u201c\u63a8\u7406\u6a21\u578b\u201d\u3002\u7b80\u5355\u70b9\u89e3\u91ca\u5c31\u662f\u628a\u5927\u6a21\u578b\u7684\u4ef7\u683c\u6253\u4e0b\u6765\u4e86\uff0c\u8ba9\u4eba\u4eba\u90fd\u7528\u5f97\u8d77\u66f4\u597d\u7684\u5927\u6a21\u578b\u3002<\/p>\n

\u4e8c\u6b21\u51fa\u5708\u662f\u5404\u7c7b\u65b0\u95fb\u62a5\u9053\u5411\u7f51\u6c11\u8f70\u70b8\uff0c\u50cf\u54ea\u5412\u7206\u706b\u4e00\u6837…<\/p>\n

\u4e09\u6b21\u51fa\u5708\u662f\u81ea\u5a92\u4f53\u501f\u52a9\u6ce2\u70ed\u70b9\u201c\u8d5a\u94b1\u201d\uff0c\u6240\u4ee5\u5927\u5bb6\u5728\u5404\u79cd\u7fa4\u91cc\u770b\u5230\u4e86\uff1a360\u4e13\u7ebfDeepSeek-R1\u3001\u6559\u4f60\u672c\u5730\u90e8\u7f72DeepSeek-R1\u3001\u6e05\u534e\u5927\u5b66\u6559\u4f60\u4f7f\u7528DeepSeek-R1<\/p>\n

\u76ee\u524d\u5230\u4e86\u6700\u540e\u4e00\u4e2a\u9636\u6bb5\uff1a\u5168\u6c11\u7528 DeepSeek-R1 \uff0c\u5728\u56fd\u4f01\u3001\u592e\u4f01\uff0c\u751a\u81f3\u533a\u53bf\u8857\u9053\uff0c\u5e94\u8be5\u6536\u5230\u8fc7\u76f8\u5173\u6d88\u606f\u3002<\/p>\n

 <\/p>\n

\u5728\u54ea\u91cc\u53ef\u4ee5\u4f7f\u7528DeepSeek-R1<\/h2>\n

\u6211\u8be2\u95ee\u8fc7\u5f88\u591a\u4eba\uff0c\u53ef\u80fd\u8fdeDeepSeek-R1\u7f51\u5740\u90fd\u627e\u4e0d\u5230\uff0c\u6709\u4e9b\u4eba\u662f\u56e0\u4e3a\u5b98\u7f51\u5361\uff0c\u5bfb\u627e\u5176\u4ed6\u63d0\u4f9b DeepSeek-R1 \u6a21\u578b\u7684\u670d\u52a1\uff0c\u4f46\u4f60\u4e86\u89e3\u7684\u53ef\u80fd\u90fd\u4e0d\u5bf9…<\/p>\n

\"\"<\/p>\n

 <\/p>\n

\u5b98\u65b9\u4f7f\u7528\u6e20\u9053<\/strong><\/p>\n

\u4ed6\u7684\u5b98\u65b9\u7f51\u5740\u662f\uff1ahttps:\/\/chat.deepseek.com\/ \u6ca1\u6709PC\u5ba2\u6237\u7aef\u7248\uff0c\u624b\u673aAPP\u53bb\u5404\u5927\u5e94\u7528\u5546\u5e97\u641c\u7d22\u201cDeepSeek\u201d\u5373\u53ef\u3002<\/p>\n

\u5176\u4ed6\u4f7f\u7528\u6e20\u9053<\/strong><\/p>\n

\u76ee\u524d\u5f88\u591aAI\u5de5\u5177\u90fd\u96c6\u6210\u4e86DeepSeek-R1\uff0c\u800c\u4ed6\u4eec\u7684\u8f93\u51fa\u8d28\u91cf\u5374\u4e0d\u4e00\u6837\uff0c\u53ea\u63a8\u8350\u4e00\u4e2a\u63a5\u8fd1\u539f\u7248\u7684\u5de5\u5177\uff0c\u907f\u514d\u9009\u62e9\u56f0\u96be\uff1a<\/p>\n

\u817e\u8baf\u5143\u5b9d\uff1ahttps:\/\/yuanbao.tencent.com\/ \u540c\u65f6\u63d0\u4f9bAPP\uff0c\u8bf7\u4ece\u5b98\u7f51\u548c\u5404\u5927\u5e94\u7528\u5546\u5e97\u641c\u7d22\u201c\u5143\u5b9d\u201d\u4e0b\u8f7d\u3002<\/p>\n

\u7f51\u9875\u7248\u4f7f\u7528\u65f6\u8bb0\u5f97\u52fe\u9009\u4ee5\u4e0b\u9009\u9879\uff1a<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

 <\/p>\n

\u672c\u5730\u7535\u8111\u3001\u624b\u673a\u4e2d\u5b89\u88c5DeepSeek-R1<\/h2>\n

\u9996\u5148\u4f60\u7684\u7535\u8111GPU\u662f\u5426\u6ee1\u8db3\u5b89\u88c5 DeepSeek-R1 \u6700\u4f4e\u8981\u6c42<\/a>\uff1f\u5982\u679c\u4f60\u5bf9GPU\u6ca1\u6709\u6982\u5ff5\uff0c\u4e0d\u8981\u8003\u8651\u672c\u5730\u5b89\u88c5\u3002<\/p>\n

\u7535\u8111\u5b89\u88c5 DeepSeek-R1<\/h3>\n

\u63a8\u8350\u4f7f\u7528 Ollama<\/a> \u5b89\u88c5\uff0c\u4ed6\u7684\u7f51\u5740\u662f\uff1ahttps:\/\/ollama.com\/ \uff0c\u76f8\u5173\u53ef\u4ee5\u5b89\u88c5\u7684\u6a21\u578b\u90fd\u5728\u8fd9\u91cc\uff1ahttps:\/\/ollama.com\/search?q=deepseek-r1 \uff0c\u5982\u679c\u4f60\u9700\u8981\u8be6\u7ec6\u6559\u7a0b\u624d\u4f1a\u5b89\u88c5\uff0c\u90a3\u4e48\u4e0d\u63a8\u8350\u672c\u5730\u5b89\u88c5\u4f7f\u7528\u3002<\/p>\n

\u4f8b\u59823060\u663e\u5361\uff0c\u52c9\u5f3a\u53ef\u4ee5\u8dd114B\uff08\u5b98\u65b9\u84b8\u998f\u7248\uff09\u6a21\u578b\uff0c\u53ef\u4ee5\u590d\u5236\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

\u5982\u679c\u4f60\u7684\u7535\u8111\u914d\u7f6e\u201c\u9ad8\u201d\uff0c\u8fd8\u60f3\u672c\u5730\u90e8\u7f72\uff0c\u63a8\u8350\u4ee5\u4e0b\u672c\u5730\u4e00\u952e\u5b89\u88c5\u5305<\/strong><\/p>\n

\u672c\u5730\u5b89\u88c5\u9700\u8981\u4e00\u5b9a\u6280\u672f\u57fa\u7840\uff0c\u8fd9\u91cc\u63d0\u4f9bDeepSeek-R1\u52a0\u804a\u5929\u754c\u9762\u672c\u5730\u4e00\u952e\u5b89\u88c5\u5305\uff1a\u907f\u5751\u6307\u5357\uff1a\u6dd8\u5b9d DeepSeek R1 \u5b89\u88c5\u5305\u4ed8\u8d39\u5012\u5356\uff1f\u514d\u8d39\u6559\u4f60\u672c\u5730\u90e8\u7f72\uff08\u9644\u4e00\u952e\u5b89\u88c5\u5305\uff09<\/a><\/p>\n

 <\/p>\n

\u5982\u679c\u4f60\u7684\u7535\u8111\u914d\u7f6e\u201c\u4f4e\u201d\uff0c\u8fd8\u60f3\u672c\u5730\u90e8\u7f72\uff0c\u63a8\u8350\u4ee5\u4e0b\u4e91\u7aef\u90e8\u7f72\u65b9\u6848<\/strong><\/p>\n

\u65e0\u9700\u672c\u5730GPU\u5373\u53ef\u79c1\u6709\u5316\u90e8\u7f72 DeepSeek-R1 32B<\/a><\/p>\n

 <\/p>\n

\u624b\u673a\u5b89\u88c5 DeepSeek-R1<\/h3>\n

\u5212\u91cd\u70b9\uff01<\/strong>\u624b\u673a\u5b89\u88c5 DeepSeek-R1\uff0c\u4f60\u7684\u76ee\u7684\u662f\u4e0b\u8f7d\u5b98\u65b9APP\uff0c\u8fd8\u662f\u8981\u8ba9 DeepSeek-R1 \u6a21\u578b\u5728\u4f60\u7684\u624b\u673a\u672c\u5730\u8fd0\u884c\uff1f\u5982\u679c\u53ea\u662f\u8981\u5728\u624b\u673a\u4e2d\u4f7f\u7528\uff0c\u5e76\u4e0d\u5f3a\u8c03\u5728\u624b\u673a\u4e2d\u672c\u5730\u8fd0\u884c\uff0c\u76f4\u63a5\u5728\u5e94\u7528\u5546\u5e97\u641c\u7d22\u201cDeepSeek\u201d\u6216\u201c\u817e\u8baf\u5143\u5b9d\u201d\uff0c\u4e0b\u8f7d\u5373\u53ef\uff0c\u4e24\u8005\u90fd\u662f\u5728\u7ebf\u4f7f\u7528\u3002\u4e0b\u9762\u4ec5\u63d0\u4f9b\u7684\u662f\u624b\u673a\u672c\u5730\u8fd0\u884c DeepSeek-R1 \u6a21\u578b\u7684\u65b9\u6cd5\u3002<\/p>\n

\u624b\u673a\u4e2d\u672c\u5730\u5b89\u88c5 DeepSeek-R1 \u7684\u7f3a\u70b9\uff1a<\/strong>\u5b89\u88c5\u7684\u6a21\u578b\u80fd\u529b\u6709\u9650\uff0c\u667a\u80fd\u5199\u4e00\u4e9b\u7b80\u5355\u7684\u6587\u6848\uff0c\u6574\u7406\u3001\u603b\u7ed3\u8d44\u6599\u3002<\/p>\n

\u5982\u679c\u4f60\u51b3\u5b9a\u5b89\u88c5\uff1a\u624b\u673a\u672c\u5730\u5b89\u88c5 DeepSeek-R1 \u6a21\u578b\u8bf4\u660e\uff0c\u9002\u7528IOS\u548c\u5b89\u5353\u9ad8\u914d\u673a\u578b<\/a><\/p>\n

\"PocketPal<\/p>\n

 <\/p>\n

DeepSeek-R1 \u9002\u5408\u505a\u4ec0\u4e48<\/h2>\n

DeepSeek-R1 \u5341\u5206\u4f18\u79c0\uff0c\u80fd\u505a\u4e8b\u60c5\u5f88\u591a\uff0c\u5148\u7528\u6392\u9664\u6cd5\uff0c\u4e86\u89e3 DeepSeek-R1 \u4e0d\u9002\u5408\u505a\u4ec0\u4e48<\/strong>\u3002\u6211\u751f\u62105000\u4e2a\u5de6\u53f3\u95ee\u9898\uff0c\u5411 DeepSeek-R1\uff08\u6ee1\u8840\u7248\uff09\u63d0\u95ee\uff0c\u5f97\u5230\u4e86\u4e00\u4e9b\u7ecf\u9a8c\uff0c\u4f9b\u5927\u5bb6\u53c2\u8003\uff1a<\/p>\n

1.\u9519\u8bef\u95ee\u9898\u4ea7\u751f\u5e7b\u89c9\u7b54\u6848\uff1aR1<\/strong>\u5e7b\u89c9\u6bd4ChatGPT\u4e25\u91cd\u3002\u800c\u666e\u901a\u4eba\u5411R1\u63d0\u95ee\uff0c\u4f1a\u6b63\u786e\u201c\u63d0\u95ee\u201d\u672c\u8eab\u5c31\u5f88\u56f0\u96be\uff0c\u6240\u4ee5\u4f60\u5f97\u5230\u7684\u7b54\u6848\u5f80\u5f80\u5e7b\u89c9\u4e1b\u751f\u3002<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

2.\u4e0d\u9002\u5408\u8fdb\u884c\u6709\u5173\u65f6\u95f4\u7ebf\u95ee\u9898\u7684\u7814\u7a76\u4efb\u52a1<\/strong>\uff0c\u95ee\u9898\u51fa\u57283\u4e2a\u5730\u65b9\uff1a\uff081\uff09\u5927\u6a21\u578b\u8bad\u7ec3\u7684\u77e5\u8bc6\u6709\u6ede\u540e\u6027\uff0c\uff082\uff09\u5c31\u7b97\u5728\u8054\u7f51\u6a21\u5f0f\u4e0b\u201c\u6df1\u5ea6\u601d\u8003\u201d\u56e0\u4e3a\u662f\u4e00\u6b21\u53ec\u56de\u7f51\u7edc\u4fe1\u606f\uff0c\u53ec\u56de\u4fe1\u606f\u6570\u91cf\u6709\u9650\uff0c\u65e0\u6cd5\u5b8c\u6574\u7684\u6536\u96c6\u6709\u5173\u65f6\u95f4\u7ebf\u95ee\u9898\u7684\u4fe1\u606f\uff0c\uff083\uff09\u601d\u8003\u88ab\u8fc7\u591a\u7684\u4e0a\u4e0b\u6587\u5e72\u6270\uff0c\u89c13<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

 <\/p>\n

3.\u6df1\u5ea6\u601d\u8003\u5bb9\u6613\u88ab\u4e0a\u4e0b\u6587\u5e72\u6270<\/strong>\uff0c\u540c\u6837\u7684\u95ee\u9898\uff0c\u5f00\u542f\u7f51\u9875\u641c\u7d22\u540e\uff0c\u56e0\u4e3a\u5f15\u5165\u4e86\u5927\u91cf\u7684\u7f51\u9875\u4fe1\u606f\uff0c\u601d\u8003\u8fc7\u7a0b\u6df7\u4e71\uff0c\u5bfc\u81f4\u5f97\u5230\u7684\u7ed3\u679c\u66f4\u5dee\u3002\u8fd9\u4e2a\u95ee\u9898\u5f88\u4e25\u91cd\u3002<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

 <\/p>\n

4.\u4e00\u6761\u660e\u786e\u7684\u63d0\u95ee\u6307\u4ee4\u662f\u5982\u4f55\u88ab\u201c\u6df1\u5ea6\u601d\u8003\u201d\u5e72\u6270\u7684<\/strong>\uff1a\u601d\u8003\u4f1a\u5ffd\u89c6\u201c\u4e3b\u8981\u6307\u4ee4\u201d\uff0c\u53cd\u800c\u91cd\u89c6\u5176\u4ed6\u4e0a\u4e0b\u6587\uff0c\u5bfc\u81f4\u601d\u8003\u9010\u6e10\u53d1\u6563\uff0c\u800c\u4e14\u7b49\u5f85\u601d\u8003\u65f6\u95f4\u8fc7\u957f\u3002<\/p>\n

\u8ba9\u6211\u4eec\u5177\u4f53\u611f\u53d7\u4e0b\u4e00\u6761\u5f88\u7b80\u5355\u7684\u4efb\u52a1\u6307\u4ee4\uff0c\u5f88\u5c11\u7684\u4e0a\u4e0b\u6587\u5982\u4f55\u88ab\u201c\u601d\u8003\u201d\u7834\u574f\u7684\uff0c\u5de6\u4fa7\u662f DeepSeek\uff0c\u53f3\u4fa7\u662f ChatGPT\u3002<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

 <\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

 <\/p>\n

5.\u5982\u679c\u53ea\u662f\u641c\u7d22\u4fe1\u606f\uff0c\u8c37\u6b4c\u3001\u767e\u5ea6\u53ef\u80fd\u5f97\u5230\u7684\u7ed3\u679c\u66f4\u597d<\/strong>\uff0c\u5982\u679c\u4f60\u60f3\u4ece\u641c\u7d22\u7ed3\u679c\u4e2d\u5f97\u5230\u66f4\u591a\u4fe1\u606f\uff0c\u90a3\u4e48\u4f7f\u7528\u641c\u7d22\u5f15\u64ce\u6548\u7387\u66f4\u9ad8\u3002\u5c24\u5176\u662f\u5728\u4fe1\u606f\u91cf\u8f83\u5927\u65f6\u3002R1\u4e0d\u4f1a\u5e2e\u4f60\u5206\u6790\u6570\u91cf\u8f83\u5927\u7684\u7f51\u9875\u4fe1\u606f\uff0c\u56e0\u4e3a\u4ed6\u7684\u5e2e\u4f60\u641c\u7d22\u7684\u4fe1\u606f\u91cf\u6709\u9650\u3001\u80fd\u8bb0\u4f4f\u7684\u4fe1\u606f\u91cf\u6709\u9650\uff0c\u7279\u522b\u662f\u5224\u65ad\u641c\u7d22\u7ed3\u679c\u5bf9\u9519\u7684\u80fd\u529b\u66f4\u6709\u9650\uff0c\u4f1a\u4e00\u80a1\u8111\u7684\u628a\u4e00\u5806\u641c\u7d22\u7ed3\u679c\u5e2e\u4f60\u8fdb\u884c\u63a8\u7406\uff0c\u7136\u540e\u7ed9\u4f60\u7b54\u6848\u3002<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

 <\/p>\n

\u590d\u6742\u4e00\u4e9b\u7684\u573a\u666f\uff0c\u6bd4\u5982\u8981\u64b0\u5199\u8bba\u6587\uff0c\u8fdb\u884c\u201c\u8d44\u6599\u7c7b\u201d\u4fe1\u606f\u6536\u96c6\u3001\u6574\u7406\uff0c\u9700\u8981\u591a\u8f6e\u4fe1\u606f\u6536\u96c6\u3001\u591a\u8f6e\u63a8\u7406\uff08\u4eba\u5de5\u64cd\u4f5c\u4e5f\u662f\u4e00\u6837\u903b\u8f91\uff09\uff0cR1\u53ea\u662f\u8fdb\u884c\u4e00\u8f6e\u4fe1\u606f\u6536\u96c6\u5e76\u63a8\u7406\uff0c\u65e0\u6cd5\u4e00\u6b21\u89e3\u51b3\u590d\u6742\u7684\u7cfb\u7edf\u6027\u95ee\u9898\u3002\u5f53\u4f60\u77e5\u9053\u8fd9\u4e00\u70b9\u540e\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u624b\u5de5\u6574\u7406\u76f8\u5173\u8d44\u6599\uff0c\u6c47\u603b\u597d\u4ee5\u540e\uff0c\u5728\u4e22\u7ed9R1\u5206\u6790\u3002<\/p>\n

 <\/p>\n

\u6b63\u786e\u4f7f\u7528 DeepSeek-R1<\/h3>\n

\u6ce8\u610f\uff1a<\/strong>\u5c31\u7b97\u4e0d\u4f7f\u7528 DeepSeek-R1 \u6a21\u578b\uff0c\u4f7f\u7528\u5176\u4ed6\u6a21\u578b\u65f6\uff0c\u5728\u95ee\u9898\u524d\u52a0\u4e00\u53e5\uff1a\u201c\u8ba9\u6211\u4eec\u4e00\u6b65\u4e00\u6b65\u601d\u8003\u201d\uff0c\u5176\u4ed6\u6a21\u578b\u4f9d\u7136\u4f1a\u7ed9\u51fa\u4f60\u8be6\u7ec6\u7684\u601d\u8003\u8fc7\u7a0b\u3002\u4f46\u63a8\u7406\u8be6\u7ec6\u7a0b\u5ea6\u548c\u6700\u7ec8\u7b54\u6848\u6709\u53ef\u80fd\u6ca1\u6709DeepSeek-R1\u597d\u3002<\/p>\n

\u5176\u5b9e DeepSeek-R1 \u7684\u4f7f\u7528\u6280\u5de7\u4e0e\u5176\u4ed6\u6a21\u578b\u533a\u522b\u5e76\u4e0d\u5927\uff0c\u4ec5\u6709\u51e0\u4e2a\u9700\u8981\u6ce8\u610f\u7684\u7ec6\u8282\u3002<\/p>\n

 <\/p>\n

1.\u8f93\u5165\u7684\u95ee\u9898\u590d\u6742\u6216\u5f00\u542f\u641c\u7d22\u540e\u5f97\u5230\u7684\u7b54\u6848\u4e0d\u6ee1\u610f\uff0c\u5c1d\u8bd5\u5173\u95ed\u201c\u6df1\u5ea6\u601d\u8003\u201d\u3002\u5173\u95ed\u540e\u4f7f\u7528\u7684\u662fV3\u6a21\u578b\uff0c\u4f9d\u7136\u5f88\u4e0d\u9519\u3002<\/p>\n

\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n

2.\u4f7f\u7528\u7b80\u5355\u7684\u6307\u4ee4\uff0c\u201c\u6df1\u5ea6\u601d\u8003\u201d\u4f1a\u5e2e\u4f60\u601d\u8003<\/p>\n

\u6b63\u786e\uff1a<\/strong>\u5e2e\u6211\u7ffb\u8bd1<\/p>\n

\u9519\u8bef\uff1a<\/strong>\u5e2e\u6211\u7ffb\u8bd1\u4e3a\u4e2d\u6587\uff0c\u4f7f\u7528\u7b26\u5408\u4e2d\u6587\u7528\u6237\u7684\u4e60\u60ef\u7684\u8bcd\u8bed\u7ffb\u8bd1\uff0c\u91cd\u8981\u7684\u540d\u8bcd\u9700\u8981\u4fdd\u7559\u539f\u6587\uff0c\u7ffb\u8bd1\u8981\u6ce8\u610f\u6392\u7248\u3002<\/p>\n

\u66f4\u591a\u9519\u8bef\u793a\u4f8b\uff1a<\/strong><\/p>\n

1.\u6211\u8fd9\u91cc\u6709\u4e00\u4efd\u975e\u5e38\u91cd\u8981\u7684\u5e02\u573a\u8c03\u7814\u62a5\u544a\uff0c\u5185\u5bb9\u5f88\u591a\uff0c\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u5e0c\u671b\u4f60\u8ba4\u771f\u4ed4\u7ec6\u5730\u9605\u8bfb\uff0c\u6df1\u5165\u601d\u8003\uff0c\u7136\u540e\u5206\u6790\u4e00\u4e0b\uff0c\u8fd9\u4efd\u62a5\u544a\u4e2d\u6700\u91cd\u8981\u7684\u5e02\u573a\u8d8b\u52bf\u662f\u4ec0\u4e48\uff1f\u6700\u597d\u80fd\u5217\u51fa\u6700\u91cd\u8981\u7684\u4e09\u4e2a\u8d8b\u52bf\uff0c\u5e76\u89e3\u91ca\u4e00\u4e0b\u4e3a\u4ec0\u4e48\u8ba4\u4e3a\u8fd9\u4e09\u4e2a\u8d8b\u52bf\u6700\u91cd\u8981\u3002<\/p>\n

2.\u4ee5\u4e0b\u662f\u4e00\u4e9b\u75be\u75c5\u8bca\u65ad\u7684\u4f8b\u5b50\uff1a[\u793a\u4f8b1]\uff0c[\u793a\u4f8b2]\uff0c\u73b0\u5728\u8bf7\u4f60\u6839\u636e\u4ee5\u4e0b\u75c5\u5386\u4fe1\u606f\uff0c\u8bca\u65ad\u60a3\u8005\u53ef\u80fd\u60a3\u6709\u7684\u75be\u75c5\u3002[\u7c98\u8d34\u75c5\u5386\u4fe1\u606f]<\/p><\/blockquote>\n

 <\/p>\n

3.\u4f7f\u7528\u590d\u6742\u7684\u6307\u4ee4\uff0c\u6fc0\u6d3b\u201c\u6df1\u5ea6\u601d\u8003\u201d\uff08\u6ca1\u6709\u4e00\u5b9a\u7ecf\u9a8c\u4e0d\u5efa\u8bae\u6784\u9020\u590d\u6742\u6307\u4ee4\uff0c\u590d\u6742\u6307\u4ee4\u548c\u643a\u5e26\u8fc7\u957f\u4e0a\u4e0b\u6587\uff0c\u90fd\u4f1a\u8ba9R1\u6a21\u578b\u6df7\u4e71\uff09<\/p>\n

\u6b63\u786e\uff1a<\/strong>\u5e2e\u6211\u7ffb\u8bd1\u4e3a\u4e2d\u6587\uff0c\u4f7f\u7528\u7b26\u5408\u4e2d\u6587\u7528\u6237\u7684\u4e60\u60ef\u7684\u8bcd\u8bed\u7ffb\u8bd1\uff0c\u91cd\u8981\u7684\u540d\u8bcd\u9700\u8981\u4fdd\u7559\u539f\u6587\uff0c\u7ffb\u8bd1\u8981\u6ce8\u610f\u6392\u7248\u3002<\/p>\n

\u9519\u8bef\uff1a<\/strong>\u5e2e\u6211\u7ffb\u8bd1<\/p>\n

 <\/p>\n

\u6ce8\uff1a\u8981\u8fa9\u8bc1\u7684\u770b\u5f852\u30013\u4ea7\u751f\u7684\u51b2\u7a81\uff0c\u5148\u4ece\u7b80\u5355\u7684\u6307\u4ee4\u5f00\u59cb\u5c1d\u8bd5\uff0c\u5f53\u7b54\u6848\u4e0d\u6ee1\u8db3\u7279\u5b9a\u8981\u6c42\u65f6\uff0c\u518d\u9002\u5f53\u589e\u52a0\u6307\u4ee4\u6761\u4ef6\u3002<\/p>\n

 <\/p>\n

\u4f7f\u7528\u4ee5\u4e0b\u6587\u672c\u81ea\u884c\u6d4b\u8bd5<\/strong><\/p>\n

**# How does better chunking lead to high-quality responses?\r\n**If you\u2019re reading this, I can assume you know what chunking and RAG<\/a> are. Nonetheless, here is what it is, in short.**\r\n**LLMs are trained on massive public datasets. Yet, they aren\u2019t updated afterward. Therefore, LLMs don\u2019t know anything after the pretraining cutoff date. Also, your use of LLM can be about your organization\u2019s private data, which the LLM had no way of knowing.**\r\n**Therefore, a beautiful solution called RAG has emerged. RAG asks the LLM to ** answer questions based on the context provided in the prompt itself** . We even ask it not to answer even if the LLM knows the answer, but the provided context is insufficient.**\r\n**How do we get the context? You can query your database and the Internet, skim several pages of a PDF report, or do anything else.**\r\n**But there are two problems in RAGs.**\r\n* **LLM\u2019s **context windows sizes** are limited (Not anymore \u2014 I\u2019ll get to this soon!)**\r\n* **A large context window has a high ** signal-to-noise ratio** .**\r\n**First, early LLMs had limited window sizes. GPT 2, for instance, had only a 1024 token<\/a> context window. GPT 3 came up with a 2048 token window. These are merely the **size of a typical blog post** .**\r\n**Due to these limitations, the LLM prompt cannot include an organization\u2019s entire knowledge base. Engineers were forced to reduce the size of their input to the LLM to get a good response.**\r\n**However, various models with a context window of 128k tokens<\/a> showed up. This is usually **the size of an annual report** for many listed companies. It is good enough to upload a document to a chatbot and ask questions.**\r\n**But, it didn\u2019t always perform as expected. That\u2019s because of the noise in the context. A large document easily contains many unrelated information and the necessary pieces<\/a>. This unrelated information drives the LLM to lose its objective or hallucinate.**\r\n**This is why we chunk the documents. Instead of sending a large document to the LLM, we break it into smaller pieces and only send the most relevant pieces.**\r\n**However, this is easier said than done.**\r\n**There are a million possible ways to break a document into chunks. For instance, you may break the document paragraph by paragraph, and I may do it sentence by sentence. Both are valid methods, but one may work better than the other in specific circumstances.**\r\n**However, we won\u2019t discuss sentence and paragraph breaks, as they are trivial and have little use in chunking. Instead, we will discuss slightly more complex ones that break documents for RAGs.**\r\n**In the rest of the post, I\u2019ll discuss a few chunking strategies I\u2019ve learned and applied.********<\/pre>\n
4.\u63d0\u793a\u8bcd\u6846\u67b6\u4f9d\u7136\u540e\u6548<\/p>\n
\u517b\u6210\u597d\u7684\u63d0\u793a\u8bcd\u8f93\u5165\u4e60\u60ef\uff0c\u53ea\u9700\u8981\u8f93\u5165\u4ee5\u4e0b\u56db\u4e2a\u6761\u4ef6\uff1a[\u89d2\u8272][\u8981\u5927\u6a21\u578b\u6267\u884c\u7684\u52a8\u4f5c][\u4efb\u52a1\u76ee\u6807][\u4efb\u52a1\u80cc\u666f]\uff08\u4efb\u52a1\u80cc\u666f\u4e0d\u662f\u5fc5\u8981\u7684\uff09<\/p>\n
\u4f8b\u5982\uff1a\u626e\u6f14\u516c\u6587\u4e66\u5199\u4e13\u5bb6\uff0c<\/span>\u5e2e\u6211\u5199\u4e00\u7bc7\u5173\u4e8e\u53c2\u52a0\u201c\u5e74\u5ea6\u4f18\u79c0\u5458\u5de5\u5927\u4f1a\u201d\u73b0\u573a\u6f14\u8bb2\u7684\u62a5\u544a\u3002\u8981\u6c42\u6f14\u8bb25\u5206\u949f\u5de6\u53f3\uff0c\u8bda\u6073\u8c26\u865a\u5373\u53ef\u3002<\/span>\u6211\u7684\u516c\u53f8\u540d\u53eb\u201c\u4e2d\u56fd\u77f3\u6cb9\u201d\uff0c\u4e0a\u7ea7\u9886\u5bfc\u53eb\u201c\u674e\u5bcc\u8d35\u201d\uff0c\u6211\u7684\u5de5\u4f5c\u662f\u77f3\u6cb9\u52d8\u63a2\uff0c\u8fd9\u6b21\u5f97\u5956\u7684\u539f\u56e0\u662f\u5458\u5de5\u6295\u7968\u7b2c\u4e8c\u540d\u3002<\/span><\/p>\n
\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n
 <\/p>\n
5.\u5b66\u4f1a\u8ba9\u5927\u6a21\u578b\u5e2e\u52a9\u4f60\u63d0\u95ee\uff0c\u597d\u7684\u95ee\u9898\u624d\u6709\u597d\u7684\u7b54\u6848<\/p>\n
\u56de\u987e\u7b2c\u201c4\u201d\u70b9\uff0c\u7ed9\u51fa\u7684\u63d0\u793a\u8bcd\u793a\u4f8b\uff0c\u6709\u6ca1\u6709\u53d1\u73b0\u95ee\u9898\uff1f<\/p>\n
\u63cf\u8ff0\u7684\u4e0d\u591f\u8be6\u7ec6\uff0c\u5199\u51fa\u7684\u62a5\u544a\u4e0d\u80fd\u76f4\u63a5\u7528\uff0c\u5927\u591a\u6570\u4eba\u4f7f\u7528\u5927\u6a21\u578b\u96be\u70b9\u5c31\u662f\u4e0d\u4f1a\u63d0\u95ee\u6216\u8005\u4e0d\u613f\u610f\u8d39\u8111\u5b50\u8865\u5145\u95ee\u9898\u3002<\/p>\n
\u5176\u5b9e\u95ee\u9898\u5f88\u7b80\u5355\uff0c\u5728\u6784\u9020\u4e00\u4e2a\u5b8c\u7f8e\u7684\u95ee\u9898\u524d\uff0c\u5148\u5b66\u4f1a\u5411\u5927\u6a21\u578b\u8bf7\u6559\uff0c\u8ba9\u4ed6\u5e2e\u4f60\u5b8c\u5584\u63d0\u95ee\u3002<\/p>\n
\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n
 <\/p>\n
6.\u63d0\u95ee\u8981\u6709\u6307\u5411\u6027\uff0c\u6216\u8005\u8ba9R1\u601d\u8003\u8fc7\u7a0b\u6709\u6307\u5411\u6027\uff0c\u8fd9\u662f\u5f88\u8001\u7684\u65b9\u6cd5\uff0c\u4e0d\u4ec5\u9002\u7528\u4e8eR1<\/p>\n
\u4ee5\u4e0b\u65b9\u6cd5\u6b63\u5e38\u6765\u8bf4\u4e0d\u9700\u8981\u7528\u5728R1\u8fd9\u7c7b\u63a8\u7406\u6a21\u578b\u4e2d\u4f7f\u7528\uff0c\u4f46\u5177\u4f53\u95ee\u9898\u5177\u4f53\u5206\u6790\uff0c\u5982\u679c\u4f60\u7684\u95ee\u9898\u975e\u5e38\u6709\u6307\u5411\u6027\uff0c\u53ef\u4ee5\u5728\u95ee\u9898\u4e2d\u52a0\u5165\u4e00\u4e9b\u63cf\u8ff0\u903b\u8f91\u7b80\u5355\u3001\u7b80\u77ed\u7684\u4e0a\u4e0b\u6587<\/strong>\u3002<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
Prompt_ID<\/th>\nType<\/th>\nTrigger Sentence<\/th>\n\u4e2d\u6587<\/th>\n<\/tr>\n
101<\/td>\nCoT<\/td>\nLet’s think step by step.<\/td>\n\u6211\u4eec\u4e00\u6b65\u4e00\u6b65\u5730\u601d\u8003\u3002<\/td>\n<\/tr>\n
201<\/td>\nPS<\/td>\nLet’s first understand the problem and devise a plan to solve the problem. Then, let’s carry out the plan to solve the problem step by step.<\/td>\n\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u7406\u89e3\u95ee\u9898\u5e76\u5236\u5b9a\u89e3\u51b3\u95ee\u9898\u7684\u8ba1\u5212\u3002\u7136\u540e\uff0c\u8ba9\u6211\u4eec\u6309\u8ba1\u5212\u4e00\u6b65\u4e00\u6b65\u5730\u89e3\u51b3\u95ee\u9898\u3002<\/td>\n<\/tr>\n
301<\/td>\nPS+<\/td>\nLet’s first understand the problem, extract relevant variables and their corresponding numerals, and devise a plan. Then, let’s carry out the plan, calculate intermediate variables (pay attention to correct numeral calculation and commonsense), solve the problem step by step, and show the answer.<\/td>\n\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u7406\u89e3\u95ee\u9898\uff0c\u63d0\u53d6\u76f8\u5173\u7684\u53d8\u91cf\u548c\u5b83\u4eec\u5bf9\u5e94\u7684\u6570\u503c\uff0c\u7136\u540e\u5236\u5b9a\u4e00\u4e2a\u8ba1\u5212\u3002\u63a5\u4e0b\u6765\uff0c\u6267\u884c\u8ba1\u5212\uff0c\u8ba1\u7b97\u4e2d\u95f4\u53d8\u91cf\uff08\u6ce8\u610f\u6b63\u786e\u7684\u6570\u5b57\u8ba1\u7b97\u548c\u5e38\u8bc6\uff09\uff0c\u4e00\u6b65\u4e00\u6b65\u5730\u89e3\u51b3\u95ee\u9898\uff0c\u5e76\u663e\u793a\u7b54\u6848\u3002<\/td>\n<\/tr>\n
302<\/td>\nPS+<\/td>\nLet’s first understand the problem, extract relevant variables and their corresponding numerals, and devise a complete plan. Then, let’s carry out the plan, calculate intermediate variables (pay attention to correct numerical calculation and commonsense), solve the problem step by step, and show the answer.<\/td>\n\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u7406\u89e3\u95ee\u9898\uff0c\u63d0\u53d6\u76f8\u5173\u53d8\u91cf\u53ca\u5176\u5bf9\u5e94\u7684\u6570\u503c\uff0c\u5e76\u5236\u5b9a\u4e00\u4e2a\u5b8c\u6574\u7684\u8ba1\u5212\u3002\u7136\u540e\uff0c\u6267\u884c\u8ba1\u5212\uff0c\u8ba1\u7b97\u4e2d\u95f4\u53d8\u91cf\uff08\u6ce8\u610f\u6b63\u786e\u7684\u6570\u503c\u8ba1\u7b97\u548c\u5e38\u8bc6\uff09\uff0c\u4e00\u6b65\u4e00\u6b65\u89e3\u51b3\u95ee\u9898\uff0c\u5e76\u663e\u793a\u7b54\u6848\u3002<\/td>\n<\/tr>\n
303<\/td>\nPS+<\/td>\nLet’s devise a plan and solve the problem step by step.<\/td>\n\u8ba9\u6211\u4eec\u5236\u5b9a\u4e00\u4e2a\u8ba1\u5212\u5e76\u4e00\u6b65\u4e00\u6b65\u5730\u89e3\u51b3\u95ee\u9898\u3002<\/td>\n<\/tr>\n
304<\/td>\nPS+<\/td>\nLet’s first understand the problem and devise a complete plan. Then, let’s carry out the plan and reason problem step by step. Every step answer the subquestion, “does the person flip and what is the coin’s current state?”. According to the coin’s last state, give the final answer (pay attention to every flip and the coin\u2019s turning state).<\/td>\n\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u7406\u89e3\u95ee\u9898\u5e76\u5236\u5b9a\u4e00\u4e2a\u5b8c\u6574\u7684\u8ba1\u5212\u3002\u7136\u540e\uff0c\u6267\u884c\u8ba1\u5212\u5e76\u9010\u6b65\u89e3\u51b3\u95ee\u9898\u3002\u6bcf\u4e00\u6b65\u56de\u7b54\u5b50\u95ee\u9898\uff0c\u201c\u4eba\u662f\u5426\u7ffb\u8f6c\uff0c\u786c\u5e01\u5f53\u524d\u72b6\u6001\u662f\u4ec0\u4e48?”. \u6839\u636e\u786c\u5e01\u7684\u6700\u540e\u72b6\u6001\uff0c\u7ed9\u51fa\u6700\u7ec8\u7b54\u6848\uff08\u6ce8\u610f\u6bcf\u6b21\u7ffb\u8f6c\u548c\u786c\u5e01\u7684\u7ffb\u8f6c\u72b6\u6001\uff09\u3002<\/td>\n<\/tr>\n
305<\/td>\nPS+<\/td>\nLet’s first understand the problem, extract relevant variables and their corresponding numerals, and make a complete plan. Then, let’s carry out the plan, calculate intermediate variables (pay attention to correct numerical calculation and commonsense), solve the problem step by step, and show the answer.<\/td>\n\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u7406\u89e3\u95ee\u9898\uff0c\u63d0\u53d6\u76f8\u5173\u53d8\u91cf\u53ca\u5176\u5bf9\u5e94\u7684\u6570\u503c\uff0c\u5e76\u5236\u5b9a\u4e00\u4e2a\u5b8c\u6574\u7684\u8ba1\u5212\u3002\u7136\u540e\uff0c\u6267\u884c\u8ba1\u5212\uff0c\u8ba1\u7b97\u4e2d\u95f4\u53d8\u91cf\uff08\u6ce8\u610f\u6b63\u786e\u7684\u6570\u503c\u8ba1\u7b97\u548c\u5e38\u8bc6\uff09\uff0c\u4e00\u6b65\u4e00\u6b65\u89e3\u51b3\u95ee\u9898\uff0c\u5e76\u663e\u793a\u7b54\u6848\u3002<\/td>\n<\/tr>\n
306<\/td>\nPS+<\/td>\nLet’s first prepare relevant information and make a plan. Then, let’s answer the question step by step (pay attention to commonsense and logical coherence).<\/td>\n\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u51c6\u5907\u76f8\u5173\u4fe1\u606f\u5e76\u5236\u5b9a\u4e00\u4e2a\u8ba1\u5212\u3002\u7136\u540e\uff0c\u4e00\u6b65\u4e00\u6b65\u5730\u56de\u7b54\u95ee\u9898\uff08\u6ce8\u610f\u5e38\u8bc6\u548c\u903b\u8f91\u8fde\u8d2f\u6027\uff09\u3002<\/td>\n<\/tr>\n
307<\/td>\nPS+<\/td>\nLet’s first understand the problem, extract relevant variables and their corresponding numerals, and make and devise a complete plan. Then, let’s carry out the plan, calculate intermediate variables (pay attention to correct numerical calculation and commonsense), solve the problem step by step, and show the answer.<\/td>\n\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u7406\u89e3\u95ee\u9898\uff0c\u63d0\u53d6\u76f8\u5173\u53d8\u91cf\u53ca\u5176\u5bf9\u5e94\u7684\u6570\u503c\uff0c\u5e76\u5236\u5b9a\u4e00\u4e2a\u5b8c\u6574\u7684\u8ba1\u5212\u3002\u7136\u540e\uff0c\u6267\u884c\u8ba1\u5212\uff0c\u8ba1\u7b97\u4e2d\u95f4\u53d8\u91cf\uff08\u6ce8\u610f\u6b63\u786e\u7684\u6570\u503c\u8ba1\u7b97\u548c\u5e38\u8bc6\uff09\uff0c\u4e00\u6b65\u4e00\u6b65\u89e3\u51b3\u95ee\u9898\uff0c\u5e76\u663e\u793a\u7b54\u6848\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
 <\/p>\n
7.\u6392\u9664\u95ee\u9898\u6b67\u4e49<\/p>\n
\u8981\u6839\u636e\u7ecf\u9a8c\u4e3b\u52a8\u6392\u9664\u95ee\u9898\u53ef\u80fd\u7684\u6b67\u4e49\uff0c\u56e0\u4e3a\u63a8\u7406\u5927\u6a21\u578b\u613f\u610f\u5e2e\u52a9\u4f60\u201c\u5047\u8bbe\u201d\u95ee\u9898\uff0c\u6bd4\u5982\u4e4b\u524d\u63d0\u8fc7\u7684\u9519\u8bef\u793a\u4f8b\uff0c\u9519\u8bef\u95ee\u9898\u5e26\u6765\u9519\u8bef\u5047\u8bbe\uff0c\u7b54\u6848\u4e5f\u662f\u9519\u8bef\u7684\u3002<\/p>\n
\"\u6559\u4f1a\u666e\u901a\u4eba\u6b63\u786e\u8ba4\u8bc6\u5e76\u4f7f\u7528<\/p>\n
 <\/p>\n
8.\u5728\u8f93\u5165\u957f\u5ea6\u548c\u63a8\u7406\u6df1\u5ea6\u4e24\u8005\u4e4b\u95f4\u53ea\u80fd\u4e8c\u9009\u4e00<\/p>\n
\u8f93\u5165\u8fc7\u957f\u7684\u5185\u5bb9\u6291\u5236\u63a8\u7406\u80fd\u529b\u4e14\u8ba9\u63a8\u7406\u95ee\u9898\u53d1\u6563\uff0c\u8f93\u5165\u7b80\u77ed\u7684\u5185\u5bb9\u4f1a\u5f3a\u5316\u63a8\u7406\u80fd\u529b\u4e14\u4fdd\u6301\u4e13\u6ce8\uff0c\u4e24\u8005\u4e92\u65a5\u3002<\/p>\n
 <\/p>\n
9.\u63a7\u5236\u8f93\u51fa\u5185\u5bb9\u683c\u5f0f<\/p>\n
\u53c2\u8003\u201c4\u201d\uff0c\u63d0\u793a\u8bcd\u6846\u67b6\u3002\u6700\u540e\u4e00\u90e8\u5206\u53ef\u4ee5\u589e\u52a0[\u8f93\u51fa\u683c\u5f0f]\uff0c\u4ee5\u6b64\u7ea6\u675f\u5927\u6a21\u578b\u8f93\u51fa\u5185\u5bb9\u683c\u5f0f\u3002<\/p>\n
\u63a7\u5236\u8f93\u51fa\u5185\u5bb9\u683c\u5f0f\u5206\u4e3a\u4e24\u79cd\uff1a<\/p>\n
1.\u6392\u7248<\/strong><\/p>\n
\u4f8b1\uff1a\u4f7f\u7528markdown\u683c\u5f0f\u8f93\u51fa\uff0c\u5e76\u5bf9\u5185\u5bb9\u8fdb\u884c\u6392\u7248<\/p>\n
\u4f8b2\uff1a\u8f93\u51fa\u7684\u6587\u7ae0\u5141\u8bb8\u7c98\u8d34\u5230word\u4e2d\u4f7f\u7528<\/p>\n
2.\u6a21\u677f<\/strong><\/p>\n
\u4f8b1\uff1a\u751f\u6210\u6587\u7ae0\u8981\u5206\u4e3a\u4e09\u4e2a\u90e8\u5206\uff1a\u4ecb\u7ecd\u3001\u8bb2\u89e3\u3001\u603b\u7ed3<\/p>\n
\u4f8b2\uff1aprompt\uff1a\u5bf9\u7fa4\u804a\u3001\u4f1a\u8bae\u8bb0\u5f55\u6b64\u7c7b\u591a\u8f6e\u5bf9\u8bdd\u8fdb\u884c\u603b\u7ed3<\/a><\/p>\n
 <\/p>\n
\u5173\u4e8eDeepSeek-R1\u6269\u5c55\u9605\u8bfb<\/h2>\n
DeepSeek-R1 WebGPU\uff1a\u5728\u6d4f\u89c8\u5668\u672c\u5730\u8fd0\u884cDeepSeek R1 1.5B<\/a><\/p>\n
Tifa-DeepsexV2-7b-MGRPO\uff1a\u652f\u6301\u89d2\u8272\u626e\u6f14\u548c\u590d\u6742\u5bf9\u8bdd\u7684\u6a21\u578b\uff0c\u6027\u80fd\u8d85\u8d8a32b\uff08\u9644\u4e00\u952e\u5b89\u88c5\u5305\uff09<\/a><\/p>\n
\u57fa\u4e8eDeepSeek-R1\u5b9e\u73b0\u672c\u5730\/API\u77e5\u8bc6\u5e93\uff0c\u5e76\u63a5\u5165\u5fae\u4fe1BOT<\/a><\/p>\n
DeepSeek \u5b98\u65b9\u63a8\u8350\uff1a\u96c6\u6210 DeepSeek R1 \u7684\u5b9e\u7528AI\u5de5\u5177\u6307\u5357<\/a><\/p>\n
DeepSeek R1 \u8d8a\u72f1\uff1a\u5c1d\u8bd5\u7a81\u7834 DeepSeek \u7684\u5ba1\u67e5\u673a\u5236<\/a><\/p>\n
\u4f7f\u7528 Unsloth \u8bad\u7ec3\u60a8\u81ea\u5df1\u7684 DeepSeek R1 \u63a8\u7406\u6a21\u578b<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
DeepSeek-R1 \u5bf9\u6bd4\u5176\u4ed6\u5927\u6a21\u578b\u6ca1\u4ec0\u4e48\u7279\u522b\uff0c\u4f60\u7684\u60ca\u559c\u5728\u4e8e\u770b\u5230\u601d\u8003\u8fc7\u7a0b\u6216\u4f18\u79c0\u7684\u4e2d\u6587\u8868\u8fbe\u80fd\u529b\u3002\u5982\u679c\u4f60\u4f7f\u7528\u8fc7 ChatGPT \u89c9\u5f97\u7d22\u7136\u65e0\u5473\uff0c\u90a3\u4e48 DeepSeek-R1 \u5e26\u6765\u7684\u60ca\u559c\u53ef\u80fd\u662f\u9519\u89c9\u3002\u5982\u679c\u4f60\u6bcf\u5929\u5fd9\u7740\u5e26\u5b69\u5b50\u3001\u9001\u5916\u5356\uff0c\u6839\u672c\u6ca1\u5fc5\u8981\u5173\u6ce8De…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[182],"tags":[],"class_list":["post-26363","post","type-post","status-publish","format-standard","hentry","category-shicao"],"_links":{"self":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts\/26363","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/comments?post=26363"}],"version-history":[{"count":0,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts\/26363\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/media?parent=26363"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/categories?post=26363"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/tags?post=26363"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}