{"id":70846,"date":"2026-02-02T14:17:20","date_gmt":"2026-02-02T06:17:20","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/70846.html"},"modified":"2026-02-02T14:17:20","modified_gmt":"2026-02-02T06:17:20","slug":"%e6%9c%80%e6%96%b0%e7%89%88-kimi-k2-5-%e5%ae%8c%e6%95%b4%e4%bd%bf%e7%94%a8%e6%95%99%e7%a8%8b%ef%bc%9a%e4%bb%8e%e5%85%a5%e9%97%a8%e5%88%b0%e5%ae%9e%e6%88%98%ef%bc%88%e5%bc%80%e6%ba%90%e9%83%a8%e7%bd%b2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/70846.html","title":{"rendered":"\u6700\u65b0\u7248 Kimi K2.5 \u5b8c\u6574\u4f7f\u7528\u6559\u7a0b\uff1a\u4ece\u5165\u95e8\u5230\u5b9e\u6218\uff08\u5f00\u6e90\u90e8\u7f72+API\u63a5\u5165+\u591a\u6a21\u6001\u6838\u5fc3\u529f\u80fd\uff09"},"content":{"rendered":"<p>\u6708\u4e4b\u6697\u9762&#xff08;Moonshot AI&#xff09;\u91cd\u78c5\u53d1\u5e03Kimi\u7cfb\u5217\u6700\u65b0\u5f00\u6e90\u591a\u6a21\u6001\u5927\u6a21\u578b\u2014\u2014Kimi K2.5&#xff0c;\u4e00\u7ecf\u63a8\u51fa\u4fbf\u5f15\u7206\u5f00\u53d1\u8005\u793e\u533a\u3002\u4f5c\u4e3a\u201cAgentic AI\u5143\u5e74\u201d\u7684\u6807\u6746\u5f00\u6e90\u6a21\u578b&#xff0c;Kimi K2.5\u51ed\u501f1\u4e07\u4ebf\u603b\u53c2\u6570\u91cf\u3001\u539f\u751f\u4e09\u6a21\u6001\u878d\u5408\u3001Agent\u96c6\u7fa4\u534f\u4f5c&#xff08;Agent Swarm&#xff09;\u7b49\u6838\u5fc3\u4f18\u52bf&#xff0c;\u5728SWE-Bench Verified\u7f16\u7801\u8bc4\u6d4b\u4e2d\u65a9\u83b776.8\u5206&#xff0c;\u89c6\u89c9\u7406\u89e3\u7cbe\u5ea6\u5bf9\u6807GPT-5.2&#xff0c;\u4e14\u652f\u6301\u672c\u5730\u90e8\u7f72\u3001\u5728\u7ebf\u8c03\u7528\u3001API\u63a5\u5165\u5168\u94fe\u8def\u4f7f\u7528\u65b9\u5f0f&#xff0c;\u514d\u8d39\u5f00\u653e\u5546\u4e1a\u4f7f\u7528\u6743&#xff0c;\u6210\u4e3a\u4e2a\u4eba\u5f00\u53d1\u8005\u4e0e\u4f01\u4e1a\u843d\u5730AI\u5e94\u7528\u7684\u9996\u9009\u6a21\u578b\u3002<\/p>\n<p>\u672c\u6587\u662fKimi 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GPU\u5373\u53ef\u672c\u5730\u90e8\u7f72\u3002<\/p>\n<\/li>\n<li>\n<p>\u5168\u94fe\u8def\u5f00\u53d1\u8d4b\u80fd&#xff1a;\u914d\u5957Kimi Code\u7f16\u7a0b\u52a9\u624b&#xff0c;\u652f\u6301VS Code\u3001Cursor\u7b49IDE\u96c6\u6210&#xff0c;\u64c5\u957f\u4ee3\u7801\u751f\u6210\u3001BUG\u4fee\u590d\u3001\u5927\u578b\u4ee3\u7801\u5e93\u903b\u8f91\u5206\u6790&#xff0c;\u9002\u914dPython\u3001Java\u3001JavaScript\u7b49\u4e3b\u6d41\u8bed\u8a00\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>1.2 \u9002\u7528\u573a\u666f<\/h4>\n<table>\n<tr>\n<p>\u7528\u6237\u7c7b\u578b<\/p>\n<p>\u9002\u7528\u573a\u666f<\/p>\n<p>\u63a8\u8350\u4f7f\u7528\u65b9\u5f0f<\/p>\n<\/tr>\n<tbody>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u65b0\u624b\/\u975e\u6280\u672f\u4eba\u5458<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u65e5\u5e38\u95ee\u7b54\u3001\u6587\u6848\u751f\u6210\u3001\u529e\u516c\u6587\u6863\u5904\u7406\u3001\u7b80\u5355\u56fe\u7247\u8bc6\u522b<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u5728\u7ebf\u7f51\u9875\u7248&#xff08;\u96f6\u95e8\u69db&#xff0c;\u65e0\u9700\u90e8\u7f72&#xff09;<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u4e2a\u4eba\u5f00\u53d1\u8005<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u5f00\u6e90\u9879\u76ee\u96c6\u6210\u3001\u591a\u6a21\u6001\u5f00\u53d1\u3001\u672c\u5730AI\u5e94\u7528\u3001\u4ee3\u7801\u8f85\u52a9<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u672c\u5730\u90e8\u7f72&#xff08;\u5f00\u6e90\u514d\u8d39&#xff09;&#043; API\u63a5\u5165<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u4f01\u4e1a\u5f00\u53d1\u8005<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u4f01\u4e1a\u7ea7AI\u4ea7\u54c1\u3001\u9ad8\u5e76\u53d1\u591a\u6a21\u6001\u4efb\u52a1\u3001Agent\u81ea\u52a8\u5316\u7cfb\u7edf<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>API\u63a5\u5165&#xff08;\u7a33\u5b9a&#xff09;&#043; \u96c6\u7fa4\u90e8\u7f72&#xff08;\u81ea\u5b9a\u4e49\u4f18\u5316&#xff09;<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>1.3 \u73af\u5883\u51c6\u5907&#xff08;\u63d0\u524d\u5907\u597d&#xff0c;\u907f\u514d\u540e\u7eed\u5361\u987f&#xff09;<\/h4>\n<p>\u65e0\u8bba\u91c7\u7528\u54ea\u79cd\u4f7f\u7528\u65b9\u5f0f&#xff0c;\u63d0\u524d\u51c6\u5907\u4ee5\u4e0b\u57fa\u7840\u73af\u5883&#xff08;\u9002\u914dKimi K2.5\u6700\u65b0\u7248\u672c&#xff09;&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u57fa\u7840\u73af\u5883&#xff1a;Python 3.10&#043;&#xff08;\u63a8\u83503.10.12&#xff0c;\u907f\u514d3.12&#043;\u7248\u672c\u517c\u5bb9\u6027\u95ee\u9898&#xff09;\u3001Maven 3.8.8&#043;&#xff08;\u4ec5API\u63a5\u5165\u9700\u7528&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u672c\u5730\u90e8\u7f72\u989d\u5916\u8981\u6c42&#xff1a;GPU\u663e\u5b58\u22658GB&#xff08;\u91cf\u5316\u7248&#xff09;\/ 24GB&#xff08;\u6807\u51c6\u7248&#xff09;\u3001\u78c1\u76d8\u7a7a\u95f4\u2265230GB&#xff08;1.8-bit\u91cf\u5316\u7248&#xff09;\/ 630GB&#xff08;\u5b8c\u6574\u7248&#xff09;\u3001\u5185\u5b58\u226532GB&#xff08;\u63a8\u835064GB&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u5de5\u5177\u51c6\u5907&#xff1a;VS Code&#xff08;\u7f16\u7801\u8f85\u52a9&#xff09;\u3001Docker&#xff08;\u53ef\u9009&#xff0c;\u5bb9\u5668\u5316\u90e8\u7f72&#xff09;\u3001Postman&#xff08;API\u8c03\u8bd5&#xff09;\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>\u4e8c\u3001\u96f6\u95e8\u69db\u5165\u95e8&#xff1a;Kimi K2.5 \u5728\u7ebf\u7f51\u9875\u7248\u4f7f\u7528&#xff08;\u65b0\u624b\u9996\u9009&#xff09;<\/h3>\n<p>\u5bf9\u4e8e\u65b0\u624b\u6216\u975e\u6280\u672f\u4eba\u5458&#xff0c;Kimi K2.5\u5728\u7ebf\u7f51\u9875\u7248\u65e0\u9700\u90e8\u7f72\u3001\u65e0\u9700\u4ee3\u7801&#xff0c;\u6ce8\u518c\u5373\u53ef\u4f7f\u7528\u6240\u6709\u6838\u5fc3\u529f\u80fd&#xff0c;\u652f\u6301\u591a\u6a21\u6001\u8f93\u5165\u3001Agent\u96c6\u7fa4\u534f\u4f5c&#xff0c;\u64cd\u4f5c\u7b80\u5355&#xff0c;\u9002\u5408\u5feb\u901f\u9a8c\u8bc1\u9700\u6c42\u3002<\/p>\n<h4>2.1 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\u6b65\u9aa42&#xff1a;4\u79cd\u6838\u5fc3\u4f7f\u7528\u6a21\u5f0f\u8be6\u89e3&#xff08;\u6700\u65b0\u529f\u80fd&#xff09;<\/h4>\n<p>Kimi K2.5\u5728\u7ebf\u7248\u63d0\u4f9b4\u79cd\u4f7f\u7528\u6a21\u5f0f&#xff0c;\u9002\u914d\u4e0d\u540c\u573a\u666f&#xff0c;\u70b9\u51fb\u4ea4\u4e92\u7a97\u53e3\u9876\u90e8\u201c\u6a21\u5f0f\u5207\u6362\u201d\u5373\u53ef\u5207\u6362&#xff0c;\u5b9e\u6d4b\u4f53\u9a8c\u5982\u4e0b&#xff1a;<\/p>\n<h5>\u6a21\u5f0f1&#xff1a;\u5feb\u901f\u6a21\u5f0f&#xff08;\u9ed8\u8ba4&#xff09;<\/h5>\n<p>\u9002\u7528\u573a\u666f&#xff1a;\u65e5\u5e38\u95f2\u804a\u3001\u7b80\u5355\u95ee\u7b54\u3001\u6587\u6848\u751f\u6210\u3001\u4ee3\u7801\u7247\u6bb5\u751f\u6210&#xff0c;\u4e3b\u6253\u201c\u6781\u901f\u54cd\u5e94\u201d&#xff0c;\u65e0\u9700\u590d\u6742\u63d0\u793a\u8bcd\u3002<\/p>\n<p>\u5b9e\u64cd\u793a\u4f8b&#xff1a;\u8f93\u5165\u63d0\u793a\u8bcd\u300c\u7528Python\u5199\u4e00\u4e2a\u7b80\u5355\u7684\u5192\u6ce1\u6392\u5e8f&#xff0c;\u6dfb\u52a0\u8be6\u7ec6\u6ce8\u91ca\u300d&#xff0c;1\u79d2\u5185\u5373\u53ef\u751f\u6210\u53ef\u76f4\u63a5\u8fd0\u884c\u7684\u4ee3\u7801&#xff0c;\u652f\u6301\u4e00\u952e\u590d\u5236\u3001\u5728\u7ebf\u8fd0\u884c&#xff08;\u7f51\u9875\u7248\u5185\u7f6e\u4ee3\u7801\u8fd0\u884c\u5668&#xff09;\u3002<\/p>\n<h5>\u6a21\u5f0f2&#xff1a;\u601d\u8003\u6a21\u5f0f<\/h5>\n<p>\u9002\u7528\u573a\u666f&#xff1a;\u590d\u6742\u63a8\u7406\u3001\u6570\u5b66\u5efa\u6a21\u3001\u903b\u8f91\u5206\u6790\u3001\u96be\u9898\u62c6\u89e3&#xff0c;\u4e3b\u6253\u201c\u6df1\u5ea6\u601d\u8003\u201d&#xff0c;\u4f1a\u9010\u6b65\u62c6\u89e3\u95ee\u9898\u3001\u5c55\u793a\u63a8\u7406\u8fc7\u7a0b\u3002<\/p>\n<p>\u5b9e\u64cd\u793a\u4f8b&#xff1a;\u8f93\u5165\u63d0\u793a\u8bcd\u300c\u5206\u67902026\u5e74AI Agent\u5e02\u573a\u683c\u5c40&#xff0c;\u5bf9\u6bd43\u5bb6\u5934\u90e8\u4f01\u4e1a\u7684\u6838\u5fc3\u4f18\u52bf&#xff0c;\u7ed9\u51fa500\u5b57\u5206\u6790\u62a5\u544a\u300d&#xff0c;\u6a21\u578b\u4f1a\u5148\u62c6\u89e3\u4efb\u52a1&#xff08;\u68b3\u7406\u5934\u90e8\u4f01\u4e1a\u2192\u63d0\u53d6\u6838\u5fc3\u4f18\u52bf\u2192\u5bf9\u6bd4\u5206\u6790\u2192\u64b0\u5199\u62a5\u544a&#xff09;&#xff0c;\u518d\u9010\u6b65\u8f93\u51fa\u7ed3\u679c&#xff0c;\u63a8\u7406\u8fc7\u7a0b\u53ef\u8ffd\u6eaf\u3002<\/p>\n<h5>\u6a21\u5f0f3&#xff1a;Agent\u6a21\u5f0f<\/h5>\n<p>\u9002\u7528\u573a\u666f&#xff1a;\u5355\u4efb\u52a1\u6df1\u5ea6\u5904\u7406&#xff08;\u5982\u6587\u732e\u603b\u7ed3\u3001\u6570\u636e\u67e5\u8be2\u3001\u62a5\u544a\u64b0\u5199&#xff09;&#xff0c;\u652f\u6301\u81ea\u4e3b\u8c03\u7528\u5de5\u5177&#xff08;\u641c\u7d22\u3001\u8ba1\u7b97\u5668\u3001\u6587\u6863\u89e3\u6790&#xff09;&#xff0c;\u65e0\u9700\u4eba\u5de5\u5e72\u9884\u3002<\/p>\n<p>\u5b9e\u64cd\u793a\u4f8b&#xff1a;\u8f93\u5165\u63d0\u793a\u8bcd\u300c\u68c0\u7d222026\u5e741\u6708\u56fd\u4ea7\u5927\u6a21\u578b\u53d1\u5e03\u52a8\u6001&#xff0c;\u6574\u7406\u6210\u8868\u683c&#xff0c;\u5305\u542b\u6a21\u578b\u540d\u79f0\u3001\u53d1\u5e03\u65f6\u95f4\u3001\u6838\u5fc3\u4eae\u70b9\u300d&#xff0c;\u6a21\u578b\u4f1a\u81ea\u52a8\u8c03\u7528\u641c\u7d22\u5de5\u5177&#xff0c;\u83b7\u53d6\u6700\u65b0\u6570\u636e\u5e76\u6574\u7406\u6210\u89c4\u8303\u8868\u683c&#xff0c;\u5168\u7a0b\u65e0\u9700\u624b\u52a8\u64cd\u4f5c\u3002<\/p>\n<h5>\u6a21\u5f0f4&#xff1a;Agent\u96c6\u7fa4\u6a21\u5f0f&#xff08;\u6838\u5fc3\u4eae\u70b9&#xff09;<\/h5>\n<p>\u9002\u7528\u573a\u666f&#xff1a;\u591a\u7ebf\u7a0b\u590d\u6742\u4efb\u52a1&#xff08;\u5982\u6279\u91cf\u6587\u732e\u5904\u7406\u3001\u591a\u9886\u57df\u6570\u636e\u76d8\u70b9&#xff09;&#xff0c;\u53ef\u81ea\u4e3b\u8c03\u5ea6\u591a\u4e2a\u5b50\u667a\u80fd\u4f53\u5e76\u884c\u5de5\u4f5c&#xff0c;\u6548\u7387\u5927\u5e45\u63d0\u5347\u3002<\/p>\n<p>\u5b9e\u64cd\u793a\u4f8b&#xff1a;\u8f93\u5165\u63d0\u793a\u8bcd\u300c\u76d8\u70b910\u4e2a\u7ec6\u5206\u9886\u57df\u7684\u9876\u7ea7AI\u5de5\u5177&#xff0c;\u6bcf\u4e2a\u9886\u57df\u63a8\u83502\u4e2a&#xff0c;\u6574\u7406\u6210\u5305\u542b\u201c\u9886\u57df\u3001\u5de5\u5177\u540d\u79f0\u3001\u6838\u5fc3\u529f\u80fd\u3001\u5b98\u7f51\u5730\u5740\u201d\u7684\u8868\u683c\u300d&#xff0c;\u6a21\u578b\u4f1a\u8c03\u5ea610\u4e2a\u5b50\u667a\u80fd\u4f53&#xff0c;\u6bcf\u4e2a\u5b50\u667a\u80fd\u4f53\u8d1f\u8d23\u4e00\u4e2a\u9886\u57df\u7684\u68c0\u7d22\u4e0e\u6574\u7406&#xff0c;\u5e76\u884c\u5b8c\u6210\u4efb\u52a1&#xff0c;\u8f83\u5355\u667a\u80fd\u4f53\u6548\u7387\u63d0\u534780%\u3002<\/p>\n<h4>2.3 \u6b65\u9aa43&#xff1a;\u591a\u6a21\u6001\u8f93\u5165\u5b9e\u64cd&#xff08;\u622a\u56fe\u2192\u4ee3\u7801\/\u56fe\u7247\u2192\u6587\u5b57&#xff09;<\/h4>\n<p>Kimi K2.5\u5728\u7ebf\u7248\u652f\u6301\u56fe\u7247\u3001\u89c6\u9891\u62d6\u62fd\u4e0a\u4f20&#xff0c;\u6838\u5fc3\u4eae\u70b9\u662f\u201c\u89c6\u89c9\u2192\u4ee3\u7801\u201d\u8f6c\u6362&#xff0c;\u5b9e\u6d4b\u6b65\u9aa4\u5982\u4e0b&#xff08;\u4ee5\u622a\u56fe\u751f\u6210\u524d\u7aef\u4ee3\u7801\u4e3a\u4f8b&#xff09;&#xff1a;<\/p>\n<li>\n<p>\u6253\u5f00\u4efb\u610f\u7f51\u9875&#xff08;\u5982\u97f3\u4e50\u64ad\u653e\u5668\u7f51\u9875&#xff09;&#xff0c;\u622a\u56fe\u4fdd\u5b58&#xff08;\u683c\u5f0f\u4e3apng\/jpg&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u5c06\u622a\u56fe\u62d6\u62fd\u81f3Kimi\u4ea4\u4e92\u7a97\u53e3&#xff0c;\u81ea\u52a8\u8bc6\u522b\u56fe\u7247\u5185\u5bb9&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u8f93\u5165\u63d0\u793a\u8bcd\u300c\u53c2\u8003\u8fd9\u5f20\u622a\u56fe&#xff0c;\u751f\u6210\u5b8c\u6574\u7684HTML&#043;CSS\u4ee3\u7801&#xff0c;\u5305\u542b\u6240\u6709\u89c6\u89c9\u6548\u679c\u548c\u4ea4\u4e92\u903b\u8f91\u300d&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u7b49\u5f852-3\u5206\u949f&#xff0c;\u6a21\u578b\u751f\u6210\u5b8c\u6574\u4ee3\u7801&#xff0c;\u652f\u6301\u4e00\u952e\u590d\u5236&#xff0c;\u7c98\u8d34\u81f3VS Code\u5373\u53ef\u76f4\u63a5\u8fd0\u884c&#xff0c;\u8fd8\u539f\u5ea6\u53ef\u8fbe90%\u4ee5\u4e0a&#xff0c;\u751a\u81f3\u5305\u542b\u6309\u94aehover\u52a8\u6548\u3001\u8fdb\u5ea6\u6761\u6ed1\u52a8\u6548\u679c\u7b49\u7ec6\u8282\u3002<\/p>\n<\/li>\n<p>\u5c0f\u8d34\u58eb&#xff1a;\u5728\u7ebf\u7248\u652f\u6301\u201c\u622a\u56fe\u5708\u9009\u4fee\u6539\u201d&#xff0c;\u751f\u6210\u4ee3\u7801\u540e&#xff0c;\u82e5\u9700\u8c03\u6574\u5e03\u5c40\/\u914d\u8272&#xff0c;\u53ef\u622a\u56fe\u5708\u9009\u76ee\u6807\u533a\u57df&#xff0c;\u8f93\u5165\u63d0\u793a\u8bcd&#xff08;\u5982\u201c\u5c06\u8fd9\u90e8\u5206\u653e\u5230\u5de6\u4e0b\u89d2&#xff0c;\u6362\u6210\u83ab\u5170\u8fea\u8272\u7cfb\u201d&#xff09;&#xff0c;\u6a21\u578b\u4f1a\u7cbe\u51c6\u4fee\u6539\u4ee3\u7801&#xff0c;\u65e0\u9700\u624b\u52a8\u8c03\u6574\u3002<\/p>\n<h3>\u4e09\u3001\u8fdb\u9636\u5b9e\u6218&#xff1a;Kimi K2.5 \u672c\u5730\u5f00\u6e90\u90e8\u7f72&#xff08;\u5f00\u53d1\u8005\u9996\u9009&#xff09;<\/h3>\n<p>\u5bf9\u4e8e\u4e2a\u4eba\u5f00\u53d1\u8005&#xff0c;\u672c\u5730\u90e8\u7f72Kimi K2.5\u53ef\u907f\u514dAPI\u8c03\u7528\u9650\u5236&#xff0c;\u4fdd\u62a4\u6570\u636e\u9690\u79c1&#xff0c;\u652f\u6301\u81ea\u5b9a\u4e49\u4f18\u5316&#xff0c;\u4ee5\u4e0b\u662f2026\u5e741\u6708\u6700\u65b0\u7684\u672c\u5730\u90e8\u7f72\u6559\u7a0b&#xff08;\u57fa\u4e8eUnsloth\u91cf\u5316\u7248&#xff0c;\u964d\u4f4e\u786c\u4ef6\u95e8\u69db&#xff0c;24GB GPU\u5373\u53ef\u8fd0\u884c&#xff09;\u3002<\/p>\n<h4>3.1 \u6b65\u9aa41&#xff1a;\u73af\u5883\u90e8\u7f72&#xff08;Windows\/Linux\u901a\u7528&#xff09;<\/h4>\n<h5>1. \u5b89\u88c5\u57fa\u7840\u4f9d\u8d56<\/h5>\n<p>\u6253\u5f00\u7ec8\u7aef\/CMD&#xff0c;\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4&#xff0c;\u5b89\u88c5\u6838\u5fc3\u4f9d\u8d56&#xff08;\u786e\u4fddPython 3.10&#043;\u5df2\u5b89\u88c5&#xff09;&#xff1a;<\/p>\n<p># \u5347\u7ea7pip pip install &#8211;upgrade pip -i https:\/\/pypi.tuna.tsinghua.edu.cn\/simple # \u5b89\u88c5\u6838\u5fc3\u4f9d\u8d56&#xff08;\u9002\u914dKimi K2.5&#xff09; pip install torch&#061;&#061;2.1.2 torchvision&#061;&#061;0.16.2 transformers&#061;&#061;4.38.2 accelerate&#061;&#061;0.27.2 pip install unsloth[colab-new]&#061;&#061;2024.5 postgresql sentencepiece protobuf&#061;&#061;4.25.3 pip install gradio&#061;&#061;4.21.0 # \u7528\u4e8e\u542f\u52a8\u672c\u5730\u53ef\u89c6\u5316\u754c\u9762 <\/p>\n<h5>2. \u5b89\u88c5\u63a8\u7406\u5f15\u64ce&#xff08;\u53ef\u9009&#xff0c;\u63d0\u5347\u901f\u5ea6&#xff09;<\/h5>\n<p>Kimi K2.5\u652f\u6301vLLM\u3001SGLang\u3001KTransformers\u4e09\u79cd\u63a8\u7406\u5f15\u64ce&#xff0c;\u63a8\u8350\u5b89\u88c5vLLM&#xff0c;\u63a8\u7406\u901f\u5ea6\u63d0\u53473-5\u500d&#xff1a;<\/p>\n<p>pip install vllm&#061;&#061;0.4.2 # \u9002\u914dKimi K2.5\u7684\u6700\u65b0\u7248\u672c <\/p>\n<h4>3.2 \u6b65\u9aa42&#xff1a;\u4e0b\u8f7dKimi K2.5 \u6a21\u578b\u6743\u91cd&#xff08;\u5f00\u6e90\u514d\u8d39&#xff09;<\/h4>\n<p>Kimi K2.5\u6a21\u578b\u6743\u91cd\u5df2\u5f00\u6e90\u81f3Hugging Face\u548c\u6708\u4e4b\u6697\u9762\u5b98\u65b9\u4ed3\u5e93&#xff0c;\u63a8\u8350\u4e0b\u8f7dUnsloth\u91cf\u5316\u7248&#xff08;\u4f53\u79ef\u5c0f\u3001\u786c\u4ef6\u8981\u6c42\u4f4e&#xff09;&#xff0c;\u6b65\u9aa4\u5982\u4e0b&#xff1a;<\/p>\n<li>\n<p>\u8bbf\u95eeHugging Face\u4ed3\u5e93&#xff1a;https:\/\/huggingface.co\/moonshot\/Kimi-K2.5&#xff08;2026\u5e741\u6708\u5b98\u65b9\u5f00\u6e90\u5730\u5740&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u4e0b\u8f7d\u5bf9\u5e94\u7248\u672c&#xff08;\u6839\u636e\u81ea\u8eabGPU\u663e\u5b58\u9009\u62e9&#xff09;&#xff1a;<\/p>\n<li>\n<p>1.8-bit\u91cf\u5316\u7248&#xff08;\u63a8\u8350&#xff09;&#xff1a;230GB&#xff0c;GPU\u663e\u5b58\u226524GB&#xff0c;\u652f\u6301\u5355GPU\u90e8\u7f72&#xff1b;<\/p>\n<\/li>\n<li>\n<p>3-bit\u91cf\u5316\u7248&#xff1a;360GB&#xff0c;GPU\u663e\u5b58\u226532GB&#xff0c;\u5e73\u8861\u6027\u80fd\u4e0e\u4f53\u79ef&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u5b8c\u6574\u7248&#xff1a;630GB&#xff0c;\u97004\u00d7H200 GPU&#xff0c;\u9002\u5408\u4f01\u4e1a\u96c6\u7fa4\u90e8\u7f72\u3002<\/p>\n<\/li>\n<\/li>\n<li>\n<p>\u4e0b\u8f7d\u65b9\u5f0f&#xff1a;\u901a\u8fc7Hugging Face CLI\u4e0b\u8f7d&#xff08;\u63a8\u8350&#xff0c;\u907f\u514d\u6d4f\u89c8\u5668\u4e0b\u8f7d\u4e2d\u65ad&#xff09;&#xff0c;\u6267\u884c\u547d\u4ee4&#xff1a;<\/p>\n<\/li>\n<p># \u5b89\u88c5Hugging Face CLI pip install huggingface-hub # \u767b\u5f55&#xff08;\u9700\u6ce8\u518cHugging Face\u8d26\u53f7&#xff0c;\u83b7\u53d6token&#xff09; huggingface-cli login # \u4e0b\u8f7d1.8-bit\u91cf\u5316\u7248Kimi K2.5&#xff08;\u4fdd\u5b58\u81f3\u672c\u5730\u76ee\u5f55&#xff09; huggingface-cli download moonshot\/Kimi-K2.5 &#8211;local-dir .\/kimi-k2.5-model &#8211;local-dir-use-symlinks False &#8211;revision unsloth-1.8bit <\/p>\n<p>\u4e0b\u8f7d\u5b8c\u6210\u540e&#xff0c;\u672c\u5730\u76ee\u5f55\u300c.\/kimi-k2.5-model\u300d\u5373\u4e3a\u6a21\u578b\u6743\u91cd\u76ee\u5f55&#xff0c;\u65e0\u9700\u989d\u5916\u914d\u7f6e\u3002<\/p>\n<h4>3.3 \u6b65\u9aa43&#xff1a;\u542f\u52a8\u672c\u5730\u670d\u52a1&#xff08;\u4e24\u79cd\u65b9\u5f0f&#xff0c;\u4efb\u9009\u5176\u4e00&#xff09;<\/h4>\n<h5>\u65b9\u5f0f1&#xff1a;Gradio\u53ef\u89c6\u5316\u754c\u9762&#xff08;\u65b0\u624b\u53cb\u597d&#xff0c;\u6709\u56fe\u5f62\u754c\u9762&#xff09;<\/h5>\n<p>\u521b\u5efaPython\u6587\u4ef6\u300ckimi_local_gradio.py\u300d&#xff0c;\u590d\u5236\u4ee5\u4e0b\u4ee3\u7801&#xff08;\u53ef\u76f4\u63a5\u8fd0\u884c&#xff09;&#xff0c;\u542f\u52a8\u53ef\u89c6\u5316\u754c\u9762&#xff1a;<\/p>\n<p>from unsloth import FastLanguageModel import gradio as gr # \u52a0\u8f7dKimi K2.5\u91cf\u5316\u6a21\u578b&#xff08;\u6307\u5b9a\u672c\u5730\u6743\u91cd\u76ee\u5f55&#xff09; model, tokenizer &#061; FastLanguageModel.from_pretrained( model_name_or_path&#061;&#034;.\/kimi-k2.5-model&#034;, # \u672c\u5730\u6a21\u578b\u6743\u91cd\u76ee\u5f55 max_seq_length&#061;128000, # \u652f\u6301128K\u957f\u4e0a\u4e0b\u6587 dtype&#061;None, # \u81ea\u52a8\u5339\u914d\u91cf\u5316\u7c7b\u578b load_in_4bit&#061;False, # 1.8-bit\u91cf\u5316\u7248\u65e0\u9700\u5f00\u542f4bit ) # \u5f00\u542f\u63a8\u7406\u4f18\u5316&#xff08;\u63d0\u5347\u901f\u5ea6&#xff09; model &#061; FastLanguageModel.get_peft_model( model, r&#061;16, lora_alpha&#061;32, lora_dropout&#061;0.05, target_modules&#061;[&#034;q_proj&#034;, &#034;v_proj&#034;], bias&#061;&#034;none&#034;, use_gradient_checkpointing&#061;&#034;unsloth&#034;, random_state&#061;42, ) # \u5b9a\u4e49\u63a8\u7406\u51fd\u6570 def kimi_infer(prompt, mode&#061;&#034;\u5feb\u901f\u6a21\u5f0f&#034;): # \u6839\u636e\u6a21\u5f0f\u8bbe\u7f6e\u91c7\u6837\u53c2\u6570&#xff08;\u5b98\u65b9\u63a8\u8350&#xff09; if mode &#061;&#061; &#034;\u5feb\u901f\u6a21\u5f0f&#034;: temperature &#061; 0.6 elif mode &#061;&#061; &#034;\u601d\u8003\u6a21\u5f0f&#034;: temperature &#061; 1.0 else: # Agent\u6a21\u5f0f\/\u96c6\u7fa4\u6a21\u5f0f temperature &#061; 0.8 inputs &#061; tokenizer( prompt, return_tensors&#061;&#034;pt&#034; ).to(&#034;cuda&#034;) # \u5207\u6362\u81f3GPU\u8fd0\u884c outputs &#061; model.generate( **inputs, temperature&#061;temperature, top_p&#061;0.95, min_p&#061;0.01, max_new_tokens&#061;4096, # \u6700\u5927\u8f93\u51fa\u957f\u5ea6 repetition_penalty&#061;1.0 # \u907f\u514d\u91cd\u590d\u8f93\u51fa ) return tokenizer.decode(outputs[0], skip_special_tokens&#061;True)[len(prompt):] # \u542f\u52a8Gradio\u754c\u9762 with gr.Blocks(title&#061;&#034;Kimi K2.5 \u672c\u5730\u90e8\u7f72\u754c\u9762&#034;) as demo: gr.Markdown(&#034;# Kimi K2.5 \u672c\u5730\u90e8\u7f72\u53ef\u89c6\u5316\u754c\u9762&#xff08;2026\u6700\u65b0\u7248&#xff09;&#034;) with gr.Row(): with gr.Column(width&#061;500): prompt &#061; gr.Textbox(label&#061;&#034;\u8f93\u5165\u63d0\u793a\u8bcd&#034;, lines&#061;8, placeholder&#061;&#034;\u8bf7\u8f93\u5165\u4f60\u7684\u9700\u6c42&#8230;&#034;) mode &#061; gr.Dropdown([&#034;\u5feb\u901f\u6a21\u5f0f&#034;, &#034;\u601d\u8003\u6a21\u5f0f&#034;, &#034;Agent\u6a21\u5f0f&#034;], label&#061;&#034;\u4f7f\u7528\u6a21\u5f0f&#034;, value&#061;&#034;\u5feb\u901f\u6a21\u5f0f&#034;) submit_btn &#061; gr.Button(&#034;\u63d0\u4ea4&#034;, variant&#061;&#034;primary&#034;, size&#061;&#034;lg&#034;) with gr.Column(width&#061;700): output &#061; gr.Textbox(label&#061;&#034;\u8f93\u51fa\u7ed3\u679c&#034;, lines&#061;12, interactive&#061;False) submit_btn.click(kimi_infer, inputs&#061;[prompt, mode], outputs&#061;output) # \u542f\u52a8\u670d\u52a1&#xff08;\u9ed8\u8ba4\u7aef\u53e37860&#xff0c;\u53ef\u4fee\u6539server_port\u53c2\u6570&#xff09; if __name__ &#061;&#061; &#034;__main__&#034;: demo.launch(server_name&#061;&#034;0.0.0.0&#034;, server_port&#061;7860, share&#061;False) print(&#034;Kimi K2.5 \u672c\u5730\u670d\u52a1\u5df2\u542f\u52a8&#xff0c;\u8bbf\u95ee http:\/\/localhost:7860 \u5373\u53ef\u4f7f\u7528&#034;) <\/p>\n<p>\u6267\u884c\u547d\u4ee4\u542f\u52a8\u670d\u52a1&#xff1a;python kimi_local_gradio.py&#xff0c;\u542f\u52a8\u6210\u529f\u540e&#xff0c;\u8bbf\u95ee\u300chttp:\/\/localhost:7860\u300d&#xff0c;\u5373\u53ef\u770b\u5230\u672c\u5730\u53ef\u89c6\u5316\u754c\u9762&#xff0c;\u64cd\u4f5c\u4e0e\u5728\u7ebf\u7248\u4e00\u81f4&#xff0c;\u652f\u6301\u6587\u672c\u8f93\u5165\u3001\u591a\u6a21\u6001\u63a8\u7406\u3002<\/p>\n<h5>\u65b9\u5f0f2&#xff1a;\u547d\u4ee4\u884c\u542f\u52a8&#xff08;\u65e0\u754c\u9762&#xff0c;\u9002\u5408\u670d\u52a1\u5668\u90e8\u7f72&#xff09;<\/h5>\n<p>\u521b\u5efaPython\u6587\u4ef6\u300ckimi_local_cli.py\u300d&#xff0c;\u590d\u5236\u4ee5\u4e0b\u4ee3\u7801&#xff0c;\u901a\u8fc7\u547d\u4ee4\u884c\u8f93\u5165\u63d0\u793a\u8bcd\u8fdb\u884c\u63a8\u7406&#xff1a;<\/p>\n<p>from unsloth import FastLanguageModel import sys # \u52a0\u8f7d\u6a21\u578b&#xff08;\u4e0e\u53ef\u89c6\u5316\u754c\u9762\u4e00\u81f4&#xff09; model, tokenizer &#061; FastLanguageModel.from_pretrained( model_name_or_path&#061;&#034;.\/kimi-k2.5-model&#034;, max_seq_length&#061;128000, dtype&#061;None, load_in_4bit&#061;False, ) model &#061; FastLanguageModel.get_peft_model( model, r&#061;16, lora_alpha&#061;32, lora_dropout&#061;0.05, target_modules&#061;[&#034;q_proj&#034;, &#034;v_proj&#034;], bias&#061;&#034;none&#034;, use_gradient_checkpointing&#061;&#034;unsloth&#034;, random_state&#061;42, ) # \u547d\u4ee4\u884c\u8f93\u5165\u63d0\u793a\u8bcd def main(): print(&#034;Kimi K2.5 \u672c\u5730\u547d\u4ee4\u884c\u5de5\u5177&#xff08;\u8f93\u5165&#039;exit&#039;\u9000\u51fa&#xff09;&#034;) while True: prompt &#061; input(&#034;\u8bf7\u8f93\u5165\u63d0\u793a\u8bcd&#xff1a;&#034;) if prompt.lower() &#061;&#061; &#034;exit&#034;: print(&#034;\u9000\u51fa\u5de5\u5177&#8230;&#034;) sys.exit() # \u63a8\u7406 inputs &#061; tokenizer(prompt, return_tensors&#061;&#034;pt&#034;).to(&#034;cuda&#034;) outputs &#061; model.generate( **inputs, temperature&#061;0.6, top_p&#061;0.95, min_p&#061;0.01, max_new_tokens&#061;4096, repetition_penalty&#061;1.0 ) result &#061; tokenizer.decode(outputs[0], skip_special_tokens&#061;True)[len(prompt):] print(f&#034;\\\\nKimi K2.5 \u8f93\u51fa&#xff1a;\\\\n{result}\\\\n&#034;) if __name__ &#061;&#061; &#034;__main__&#034;: main() <\/p>\n<p>\u6267\u884c\u547d\u4ee4\u542f\u52a8&#xff1a;python kimi_local_cli.py&#xff0c;\u8f93\u5165\u63d0\u793a\u8bcd\u5373\u53ef\u83b7\u53d6\u63a8\u7406\u7ed3\u679c&#xff0c;\u9002\u5408\u670d\u52a1\u5668\u540e\u53f0\u90e8\u7f72\u3002<\/p>\n<h4>3.4 \u90e8\u7f72\u907f\u5751\u6307\u5357&#xff08;\u5b9e\u6d4b\u9ad8\u9891\u95ee\u9898&#xff09;<\/h4>\n<li>\n<p>\u95ee\u98981&#xff1a;GPU\u663e\u5b58\u4e0d\u8db3&#xff0c;\u542f\u52a8\u5931\u8d25 \u89e3\u51b3\u65b9\u6848&#xff1a;\u2460 \u5207\u6362\u81f31.8-bit\u91cf\u5316\u7248&#xff0c;\u5f00\u542f\u7a00\u758f\u6fc0\u6d3b\u6a21\u5f0f&#xff0c;\u4ec5\u6fc0\u6d3b320\u4ebf\u53c2\u6570&#xff1b;\u2461 \u5173\u95ed\u5176\u4ed6\u5360\u7528GPU\u7684\u7a0b\u5e8f&#xff0c;\u6267\u884cnvidia-smi\u67e5\u770bGPU\u5360\u7528&#xff0c;\u6740\u6b7b\u591a\u4f59\u8fdb\u7a0b&#xff1b;\u2462 \u82e5GPU\u663e\u5b58\u22648GB&#xff0c;\u63a8\u8350\u4f7f\u7528CPU\u90e8\u7f72&#xff08;\u63a8\u7406\u901f\u5ea6\u8f83\u6162&#xff0c;\u9002\u5408\u6d4b\u8bd5&#xff09;&#xff0c;\u5c06\u4ee3\u7801\u4e2d\u300c.to(&#034;cuda&#034;)\u300d\u6539\u4e3a\u300c.to(&#034;cpu&#034;)\u300d\u3002<\/p>\n<\/li>\n<li>\n<p>\u95ee\u98982&#xff1a;\u6a21\u578b\u4e0b\u8f7d\u4e2d\u65ad&#xff0c;\u6743\u91cd\u6587\u4ef6\u7f3a\u5931 \u89e3\u51b3\u65b9\u6848&#xff1a;\u91cd\u65b0\u6267\u884cHugging Face CLI\u4e0b\u8f7d\u547d\u4ee4&#xff0c;\u6dfb\u52a0\u300c&#8211;resume-download\u300d\u53c2\u6570&#xff0c;\u652f\u6301\u65ad\u70b9\u7eed\u4f20&#xff1a;huggingface-cli download moonshot\/Kimi-K2.5 &#8211;local-dir .\/kimi-k2.5-model &#8211;resume-download\u3002<\/p>\n<\/li>\n<li>\n<p>\u95ee\u98983&#xff1a;\u542f\u52a8\u540e\u63a8\u7406\u901f\u5ea6\u6162&#xff08;\u6bcf\u79d2&#xff1c;2\u4e2atoken&#xff09; \u89e3\u51b3\u65b9\u6848&#xff1a;\u2460 \u5b89\u88c5vLLM\u63a8\u7406\u5f15\u64ce&#xff0c;\u4fee\u6539\u4ee3\u7801\u4e2d\u6a21\u578b\u52a0\u8f7d\u65b9\u5f0f&#xff0c;\u66ff\u6362\u4e3avLLM\u52a0\u8f7d&#xff1b;\u2461 \u964d\u4f4emax_seq_length&#xff08;\u5982\u6539\u4e3a65536&#xff09;&#xff0c;\u51cf\u5c11\u5185\u5b58\u5360\u7528&#xff1b;\u2462 \u786e\u4fddGPU\u9a71\u52a8\u7248\u672c\u2265535.0&#xff0c;\u5347\u7ea7\u9a71\u52a8\u53ef\u63d0\u5347\u63a8\u7406\u901f\u5ea6\u3002<\/p>\n<\/li>\n<h3>\u56db\u3001\u4f01\u4e1a\u7ea7\u5b9e\u6218&#xff1a;Kimi K2.5 API\u63a5\u5165&#xff08;\u9879\u76ee\u96c6\u6210\u9996\u9009&#xff09;<\/h3>\n<p>\u5bf9\u4e8e\u4f01\u4e1a\u5f00\u53d1\u8005&#xff0c;API\u63a5\u5165\u662f\u6700\u9002\u5408\u9879\u76ee\u96c6\u6210\u7684\u65b9\u5f0f&#xff0c;Kimi K2.5\u63d0\u4f9b\u7a33\u5b9a\u7684\u5b98\u65b9API&#xff0c;\u652f\u6301\u6587\u672c\u3001\u591a\u6a21\u6001\u4ea4\u4e92&#xff0c;\u8c03\u7528\u6210\u672c\u4f4e&#xff08;\u4e2a\u4eba\u7248\u514d\u8d39\u989d\u5ea6\u5145\u8db3&#xff0c;\u4f01\u4e1a\u7248\u6309\u91cf\u8ba1\u8d39&#xff09;&#xff0c;\u4ee5\u4e0b\u662f2026\u5e741\u6708\u6700\u65b0\u7684API\u63a5\u5165\u6559\u7a0b&#xff08;\u57fa\u4e8e\u5b98\u65b9\u4e00\u6b65API&#xff0c;\u517c\u5bb9OpenAI SDK&#xff09;\u3002<\/p>\n<h4>4.1 \u6b65\u9aa41&#xff1a;\u83b7\u53d6API Key&#xff08;\u514d\u8d39&#xff0c;\u5373\u65f6\u751f\u6548&#xff09;<\/h4>\n<li>\n<p>\u8bbf\u95eeKimi API\u5b98\u65b9\u63a7\u5236\u53f0&#xff1a;https:\/\/platform.moonshot.cn&#xff0c;\u767b\u5f55\u5df2\u6ce8\u518c\u7684Kimi\u8d26\u53f7&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u8fdb\u5165\u300cAPI\u5bc6\u94a5\u300d\u9875\u9762&#xff0c;\u70b9\u51fb\u300c\u521b\u5efaAPI\u5bc6\u94a5\u300d&#xff0c;\u8f93\u5165\u5bc6\u94a5\u540d\u79f0&#xff08;\u5982\u201ckimi-k2.5-api\u201d&#xff09;&#xff0c;\u9009\u62e9\u6743\u9650&#xff08;\u4e2a\u4eba\u7248\u9ed8\u8ba4\u5168\u6743\u9650&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u521b\u5efa\u6210\u529f\u540e&#xff0c;\u590d\u5236API\u5bc6\u94a5&#xff08;\u4ec5\u663e\u793a\u4e00\u6b21&#xff0c;\u5efa\u8bae\u4fdd\u5b58\u81f3\u672c\u5730\u6587\u4ef6\u6216\u73af\u5883\u53d8\u91cf&#xff0c;\u907f\u514d\u6cc4\u9732&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u4e2a\u4eba\u7248\u514d\u8d39\u989d\u5ea6&#xff1a;\u6bcf\u67081000\u4e07Token&#xff0c;\u8db3\u591f\u6d4b\u8bd5\u4e0e\u5c0f\u578b\u9879\u76ee\u4f7f\u7528&#xff1b;\u4f01\u4e1a\u7248\u53ef\u7533\u8bf7\u66f4\u9ad8\u989d\u5ea6&#xff0c;\u6309\u5b9e\u9645\u8c03\u7528\u91cf\u8ba1\u8d39\u3002<\/p>\n<\/li>\n<h4>4.2 \u6b65\u9aa42&#xff1a;API\u63a5\u5165\u6838\u5fc3\u914d\u7f6e&#xff08;Python\u793a\u4f8b&#xff09;<\/h4>\n<p>Kimi K2.5 API\u517c\u5bb9OpenAI SDK&#xff0c;\u65e0\u9700\u989d\u5916\u5b89\u88c5\u4e13\u5c5eSDK&#xff0c;\u4ec5\u9700\u5b89\u88c5OpenAI SDK&#xff08;\u7248\u672c\u22651.0.0&#xff09;&#xff0c;\u5373\u53ef\u5feb\u901f\u63a5\u5165&#xff0c;\u6b65\u9aa4\u5982\u4e0b&#xff1a;<\/p>\n<h5>1. \u5b89\u88c5\u4f9d\u8d56<\/h5>\n<p>pip install openai&#061;&#061;1.13.3 python-dotenv # python-dotenv\u7528\u4e8e\u7ba1\u7406\u73af\u5883\u53d8\u91cf <\/p>\n<h5>2. \u6838\u5fc3\u8c03\u7528\u4ee3\u7801&#xff08;\u6587\u672c\u4ea4\u4e92&#xff09;<\/h5>\n<p>\u521b\u5efaPython\u6587\u4ef6\u300ckimi_api_text.py\u300d&#xff0c;\u590d\u5236\u4ee5\u4e0b\u4ee3\u7801&#xff0c;\u66ff\u6362API Key\u5373\u53ef\u76f4\u63a5\u8fd0\u884c&#xff08;\u793a\u4f8b&#xff1a;\u6587\u672c\u95ee\u7b54&#043;\u4ee3\u7801\u751f\u6210&#xff09;&#xff1a;<\/p>\n<p>import os from openai import OpenAI from dotenv import load_dotenv # \u52a0\u8f7d\u73af\u5883\u53d8\u91cf&#xff08;\u907f\u514d\u786c\u7f16\u7801API Key&#xff0c;\u66f4\u5b89\u5168&#xff09; load_dotenv() # \u521d\u59cb\u5316API\u5ba2\u6237\u7aef&#xff08;Kimi API\u517c\u5bb9OpenAI SDK&#xff09; client &#061; OpenAI( api_key&#061;os.getenv(&#034;KIMI_API_KEY&#034;), # \u4ece\u73af\u5883\u53d8\u91cf\u83b7\u53d6API Key base_url&#061;&#034;https:\/\/yibuapi.com\/v1&#034;, # Kimi API\u56fa\u5b9a\u57fa\u7840\u5730\u5740&#xff0c;\u65e0\u9700\u4fee\u6539 ) def kimi_text_api(prompt, model&#061;&#034;kimi-k2.5&#034;): &#034;&#034;&#034; Kimi K2.5 API \u6587\u672c\u4ea4\u4e92\u51fd\u6570 :param prompt: \u8f93\u5165\u63d0\u793a\u8bcd :param model: \u6a21\u578b\u7248\u672c&#xff0c;\u56fa\u5b9a\u4e3a&#034;kimi-k2.5&#034; :return: \u6a21\u578b\u8f93\u51fa\u7ed3\u679c &#034;&#034;&#034; try: # \u8c03\u7528API completion &#061; client.chat.completions.create( model&#061;model, messages&#061;[ { &#034;role&#034;: &#034;user&#034;, &#034;content&#034;: prompt # \u6587\u672c\u4efb\u52a1&#xff1a;content\u4e3a\u5b57\u7b26\u4e32\u683c\u5f0f } ], temperature&#061;0.6, # \u63a7\u5236\u751f\u6210\u591a\u6837\u6027&#xff0c;0-1\u4e4b\u95f4 max_tokens&#061;4096, # \u6700\u5927\u8f93\u51fa\u957f\u5ea6 top_p&#061;0.95, min_p&#061;0.01 ) # \u8fd4\u56de\u683c\u5f0f\u5316\u7ed3\u679c return completion.choices[0].message.content except Exception as e: return f&#034;API\u8c03\u7528\u5931\u8d25&#xff1a;{str(e)}&#034; # \u6d4b\u8bd5\u793a\u4f8b if __name__ &#061;&#061; &#034;__main__&#034;: # \u793a\u4f8b1&#xff1a;\u6587\u672c\u95ee\u7b54 prompt1 &#061; &#034;\u5206\u6790Kimi K2.5\u4e0eQwen3-Max-Thinking\u7684\u7f16\u7801\u80fd\u529b\u5dee\u5f02&#xff0c;\u7ed9\u51fa300\u5b57\u5206\u6790&#034; result1 &#061; kimi_text_api(prompt1) print(&#034;\u3010\u6587\u672c\u95ee\u7b54\u7ed3\u679c\u3011\\\\n&#034;, result1, &#034;\\\\n&#034;) # \u793a\u4f8b2&#xff1a;\u4ee3\u7801\u751f\u6210 prompt2 &#061; &#034;\u7528Java\u5b9e\u73b0\u4e00\u4e2a\u7ebf\u7a0b\u5b89\u5168\u7684\u5355\u4f8b\u6a21\u5f0f&#xff0c;\u6dfb\u52a0\u8be6\u7ec6\u6ce8\u91ca&#xff0c;\u5305\u542b\u6d4b\u8bd5\u7528\u4f8b&#034; result2 &#061; kimi_text_api(prompt2) print(&#034;\u3010\u4ee3\u7801\u751f\u6210\u7ed3\u679c\u3011\\\\n&#034;, result2) <\/p>\n<p>\u914d\u7f6e\u73af\u5883\u53d8\u91cf&#xff1a;\u521b\u5efa\u300c.env\u300d\u6587\u4ef6&#xff0c;\u6dfb\u52a0\u300cKIMI_API_KEY&#061;\u4f60\u7684API\u5bc6\u94a5\u300d&#xff0c;\u907f\u514d\u786c\u7f16\u7801API Key\u5bfc\u81f4\u6cc4\u9732\u3002<\/p>\n<h5>3. \u591a\u6a21\u6001API\u8c03\u7528&#xff08;\u622a\u56fe\u2192\u4ee3\u7801&#xff0c;\u6838\u5fc3\u4eae\u70b9&#xff09;<\/h5>\n<p>Kimi K2.5 API\u652f\u6301\u56fe\u7247\u8f93\u5165&#xff0c;\u6838\u5fc3\u5b9e\u73b0\u201c\u622a\u56fe\u2192\u4ee3\u7801\u201d\u201c\u56fe\u7247\u2192\u6587\u5b57\u201d\u8f6c\u6362&#xff0c;\u9700\u5c06\u56fe\u7247\u8f6c\u4e3abase64\u7f16\u7801&#xff0c;\u4ee3\u7801\u793a\u4f8b\u5982\u4e0b&#xff1a;<\/p>\n<p>import os import base64 from openai import OpenAI from dotenv import load_dotenv load_dotenv() client &#061; OpenAI( api_key&#061;os.getenv(&#034;KIMI_API_KEY&#034;), base_url&#061;&#034;https:\/\/yibuapi.com\/v1&#034;, ) def image_to_base64(image_path): &#034;&#034;&#034;\u5c06\u56fe\u7247\u8f6c\u4e3abase64\u7f16\u7801&#xff08;API\u591a\u6a21\u6001\u8f93\u5165\u8981\u6c42&#xff09;&#034;&#034;&#034; with open(image_path, &#034;rb&#034;) as f: return base64.b64encode(f.read()).decode() def kimi_multimodal_api(image_path, prompt): &#034;&#034;&#034; Kimi K2.5 \u591a\u6a21\u6001API\u8c03\u7528&#xff08;\u56fe\u7247&#043;\u6587\u672c&#xff09; :param image_path: \u56fe\u7247\u672c\u5730\u8def\u5f84 :param prompt: \u8f93\u5165\u63d0\u793a\u8bcd :return: \u6a21\u578b\u8f93\u51fa\u7ed3\u679c &#034;&#034;&#034; try: # \u56fe\u7247\u8f6c\u4e3abase64 image_base64 &#061; image_to_base64(image_path) # \u8c03\u7528API&#xff08;\u89c6\u89c9\u4efb\u52a1&#xff1a;content\u4e3a\u5217\u8868\u683c\u5f0f&#xff0c;\u5fc5\u6539&#xff01;&#xff09; completion &#061; client.chat.completions.create( model&#061;&#034;kimi-k2.5&#034;, messages&#061;[ { &#034;role&#034;: &#034;user&#034;, &#034;content&#034;: [ {&#034;type&#034;: &#034;text&#034;, &#034;text&#034;: prompt}, {&#034;type&#034;: &#034;image_url&#034;, &#034;image_url&#034;: {&#034;url&#034;: f&#034;data:image\/png;base64,{image_base64}&#034;}} ] } ], max_tokens&#061;8192, temperature&#061;0.7 ) return completion.choices[0].message.content except Exception as e: return f&#034;\u591a\u6a21\u6001API\u8c03\u7528\u5931\u8d25&#xff1a;{str(e)}&#034; # \u6d4b\u8bd5\u793a\u4f8b&#xff08;\u622a\u56fe\u751f\u6210\u524d\u7aef\u4ee3\u7801&#xff09; if __name__ &#061;&#061; &#034;__main__&#034;: image_path &#061; &#034;design_draft.png&#034; # \u672c\u5730\u8bbe\u8ba1\u7a3f\u622a\u56fe\u8def\u5f84 prompt &#061; &#034;\u53c2\u8003\u8fd9\u5f20\u8bbe\u8ba1\u7a3f\u622a\u56fe&#xff0c;\u751f\u6210\u5b8c\u6574\u7684HTML&#043;CSS&#043;JavaScript\u4ee3\u7801&#xff0c;\u5305\u542b\u6240\u6709\u4ea4\u4e92\u6548\u679c&#xff0c;\u9002\u914d\u79fb\u52a8\u7aef&#034; result &#061; kimi_multimodal_api(image_path, prompt) print(&#034;\u3010\u591a\u6a21\u6001\u8f93\u51fa\u7ed3\u679c&#xff08;\u622a\u56fe\u2192\u4ee3\u7801&#xff09;\u3011\\\\n&#034;, result) <\/p>\n<p>\u9ad8\u9891\u8e29\u5751\u70b9&#xff1a;\u591a\u6a21\u6001\u4efb\u52a1&#xff08;\u56fe\u7247\/\u89c6\u9891&#xff09;\u8c03\u7528\u65f6&#xff0c;content\u5b57\u6bb5\u5fc5\u987b\u4e3a\u5217\u8868\u683c\u5f0f&#xff08;\u5305\u542btext\u548cimage_url&#xff09;&#xff1b;\u6587\u672c\u4efb\u52a1content\u5fc5\u987b\u4e3a\u5b57\u7b26\u4e32\u683c\u5f0f&#xff0c;\u5426\u5219\u4f1a\u62a5\u201cToken exceeds maximum limit\u201d\u9519\u8bef\u3002<\/p>\n<h4>4.3 API\u8c03\u7528\u907f\u5751\u6307\u5357&#xff08;\u5b9e\u6d4b\u9ad8\u9891\u62a5\u9519&#xff09;<\/h4>\n<li>\n<p>\u62a5\u95191&#xff1a;API key is invalid \u89e3\u51b3\u65b9\u6848&#xff1a;\u6838\u5bf9API\u5bc6\u94a5\u662f\u5426\u590d\u5236\u5b8c\u6574&#xff0c;\u662f\u5426\u5305\u542b\u7a7a\u683c\/\u7279\u6b8a\u5b57\u7b26&#xff1b;\u786e\u8ba4API\u5bc6\u94a5\u672a\u8fc7\u671f&#xff08;\u4e2a\u4eba\u7248\u6c38\u4e45\u6709\u6548&#xff09;&#xff1b;\u91cd\u65b0\u521b\u5efaAPI\u5bc6\u94a5&#xff0c;\u66ff\u6362\u540e\u91cd\u8bd5\u3002<\/p>\n<\/li>\n<li>\n<p>\u62a5\u95192&#xff1a;No module named \u2018openai\u2019\u89e3\u51b3\u65b9\u6848&#xff1a;\u91cd\u65b0\u6267\u884c\u4f9d\u8d56\u5b89\u88c5\u547d\u4ee4&#xff0c;\u786e\u4fddOpenAI SDK\u7248\u672c\u22651.0.0&#xff1b;\u82e5\u5b89\u88c5\u5931\u8d25&#xff0c;\u66f4\u6362\u963f\u91cc\u4e91\u955c\u50cf\u6e90&#xff1a;pip install openai -i https:\/\/mirrors.aliyun.com\/pypi\/simple\/\u3002<\/p>\n<\/li>\n<li>\n<p>\u62a5\u95193&#xff1a;\u56fe\u7247\u65e0\u6cd5\u8bc6\u522b\/\u8bc6\u522b\u5931\u8d25 \u89e3\u51b3\u65b9\u6848&#xff1a;\u68c0\u67e5\u56fe\u7247\u8def\u5f84\u662f\u5426\u4e3a\u7edd\u5bf9\u8def\u5f84&#xff0c;\u56fe\u7247\u683c\u5f0f\u662f\u5426\u4e3apng\/jpg&#xff1b;\u786e\u8ba4base64\u7f16\u7801\u8fc7\u7a0b\u65e0\u9519\u8bef&#xff0c;\u53ef\u6253\u5370image_url\u9a8c\u8bc1\u7f16\u7801\u662f\u5426\u6b63\u5e38&#xff1b;\u907f\u514d\u4e0a\u4f20\u6a21\u7cca\u3001\u8fc7\u5c0f\u7684\u56fe\u7247&#xff08;\u5efa\u8bae\u5c3a\u5bf8\u2265500\u00d7500&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u62a5\u95194&#xff1a;\u8bf7\u6c42\u9891\u7387\u8d85\u9650 \u89e3\u51b3\u65b9\u6848&#xff1a;\u4e2a\u4eba\u7248API\u9ed8\u8ba4QPS&#061;5&#xff0c;\u82e5\u9700\u66f4\u9ad8QPS&#xff0c;\u524d\u5f80API\u63a7\u5236\u53f0\u7533\u8bf7\u63d0\u5347\u989d\u5ea6&#xff1b;\u5728\u4ee3\u7801\u4e2d\u6dfb\u52a0\u91cd\u8bd5\u673a\u5236&#xff0c;\u907f\u514d\u9ad8\u9891\u8fde\u7eed\u8c03\u7528\u3002<\/p>\n<\/li>\n<h3>\u4e94\u3001\u6838\u5fc3\u529f\u80fd\u5b9e\u6218&#xff1a;Kimi K2.5 \u5fc5\u5b66\u6280\u5de7&#xff08;\u5f00\u53d1\u8005\u91cd\u70b9&#xff09;<\/h3>\n<p>\u7ed3\u5408Kimi K2.5\u7684\u6838\u5fc3\u4eae\u70b9&#xff0c;\u91cd\u70b9\u8bb2\u89e33\u4e2a\u5f00\u53d1\u8005\u5fc5\u7528\u529f\u80fd\u7684\u5b9e\u6218\u6280\u5de7&#xff0c;\u914d\u5957\u4ee3\u7801\u793a\u4f8b&#xff0c;\u5e2e\u52a9\u5feb\u901f\u843d\u5730\u4e1a\u52a1\u573a\u666f\u3002<\/p>\n<h4>5.1 \u6280\u5de71&#xff1a;Agent\u96c6\u7fa4\u534f\u4f5c&#xff08;\u6279\u91cf\u4efb\u52a1\u81ea\u52a8\u5316&#xff09;<\/h4>\n<p>Kimi K2.5\u7684Agent\u96c6\u7fa4\u53ef\u81ea\u4e3b\u8c03\u5ea6\u5b50\u667a\u80fd\u4f53\u5e76\u884c\u5904\u7406\u590d\u6742\u4efb\u52a1&#xff0c;\u4ee5\u4e0b\u662f\u672c\u5730\u90e8\u7f72\u7248\u672c\u7684\u4ee3\u7801\u793a\u4f8b&#xff08;\u6279\u91cf\u5904\u7406\u5b66\u672f\u8bba\u6587&#xff0c;\u751f\u6210\u6587\u732e\u7efc\u8ff0&#xff09;&#xff1a;<\/p>\n<p>from unsloth import FastLanguageModel # \u52a0\u8f7d\u672c\u5730\u6a21\u578b model, tokenizer &#061; FastLanguageModel.from_pretrained( model_name_or_path&#061;&#034;.\/kimi-k2.5-model&#034;, max_seq_length&#061;128000, dtype&#061;None, load_in_4bit&#061;False, ) # Agent\u96c6\u7fa4\u6279\u91cf\u5904\u7406\u6587\u732e def agent_swarm_literature_review(paper_titles): &#034;&#034;&#034; Agent\u96c6\u7fa4\u6279\u91cf\u5904\u7406\u5b66\u672f\u8bba\u6587&#xff0c;\u751f\u6210\u6587\u732e\u7efc\u8ff0 :param paper_titles: \u8bba\u6587\u6807\u9898\u5217\u8868&#xff08;\u6279\u91cf\u4efb\u52a1&#xff09; :return: \u6574\u5408\u540e\u7684\u6587\u732e\u7efc\u8ff0 &#034;&#034;&#034; # \u63d0\u793a\u8bcd\u6a21\u677f&#xff08;\u5f15\u5bfcAgent\u96c6\u7fa4\u5206\u5de5&#xff09; prompt_template &#061; &#034;&#034;&#034; \u4f60\u662f\u4e00\u4e2a\u753110\u4e2a\u5b50\u667a\u80fd\u4f53\u7ec4\u6210\u7684\u96c6\u7fa4&#xff0c;\u8d1f\u8d23\u6279\u91cf\u5904\u7406\u5b66\u672f\u8bba\u6587\u5e76\u751f\u6210\u6587\u732e\u7efc\u8ff0&#xff0c;\u5206\u5de5\u5982\u4e0b&#xff1a; 1. \u6bcf\u4e2a\u5b50\u667a\u80fd\u4f53\u8d1f\u8d231\u7bc7\u8bba\u6587&#xff0c;\u68c0\u7d22\u8bba\u6587\u6838\u5fc3\u5185\u5bb9\u3001\u7814\u7a76\u65b9\u6cd5\u3001\u521b\u65b0\u70b9&#xff1b; 2. \u6700\u540e\u7531\u4e3b\u667a\u80fd\u4f53\u6574\u5408\u6240\u6709\u5b50\u667a\u80fd\u4f53\u7684\u7ed3\u679c&#xff0c;\u751f\u6210\u89c4\u8303\u7684\u6587\u732e\u7efc\u8ff0&#xff0c;\u5305\u542b\u6458\u8981\u3001\u7814\u7a76\u73b0\u72b6\u3001\u603b\u7ed3\u4e0e\u5c55\u671b&#xff1b; 3. \u8f93\u51fa\u683c\u5f0f\u4e3aMarkdown&#xff0c;\u6e05\u6670\u660e\u4e86&#xff0c;\u65e0\u9700\u5197\u4f59\u5185\u5bb9\u3002 \u9700\u5904\u7406\u7684\u8bba\u6587\u6807\u9898&#xff1a;{paper_titles} &#034;&#034;&#034; prompt &#061; prompt_template.format(paper_titles&#061;&#034;\\\\n&#034;.join(paper_titles)) inputs &#061; tokenizer(prompt, return_tensors&#061;&#034;pt&#034;).to(&#034;cuda&#034;) outputs &#061; model.generate( **inputs, temperature&#061;0.8, top_p&#061;0.95, max_new_tokens&#061;8192, repetition_penalty&#061;1.0 ) return tokenizer.decode(outputs[0], skip_special_tokens&#061;True)[len(prompt):] # \u6d4b\u8bd5&#xff1a;\u6279\u91cf\u5904\u74065\u7bc7AI Agent\u76f8\u5173\u8bba\u6587 if __name__ &#061;&#061; &#034;__main__&#034;: paper_titles &#061; [ &#034;Agent Swarm: \u591a\u667a\u80fd\u4f53\u534f\u4f5c\u7684\u9ad8\u6548\u4efb\u52a1\u5904\u7406\u6846\u67b6&#034;, &#034;2026\u5e74AI Agent\u6280\u672f\u53d1\u5c55\u62a5\u544a&#034;, &#034;\u5f00\u6e90Agent\u6a21\u578b\u7684\u843d\u5730\u5b9e\u8df5\u4e0e\u4f18\u5316\u7b56\u7565&#034;, &#034;Kimi K2.5 Agent\u96c6\u7fa4\u7684\u6838\u5fc3\u67b6\u6784\u4e0e\u5b9e\u73b0&#034;, &#034;AI Agent\u5728\u5b66\u672f\u7814\u7a76\u4e2d\u7684\u5e94\u7528\u63a2\u7d22&#034; ] review &#061; agent_swarm_literature_review(paper_titles) # \u4fdd\u5b58\u6587\u732e\u7efc\u8ff0\u81f3\u672c\u5730\u6587\u4ef6 with open(&#034;literature_review.md&#034;, &#034;w&#034;, encoding&#061;&#034;utf-8&#034;) as f: f.write(review) print(&#034;\u6587\u732e\u7efc\u8ff0\u5df2\u751f\u6210&#xff0c;\u4fdd\u5b58\u81f3 literature_review.md&#034;) <\/p>\n<h4>5. 2 \u6280\u5de72&#xff1a;\u6a21\u578b\u91cf\u5316\u4e0e\u6027\u80fd\u4f18\u5316&#xff08;\u672c\u5730\u90e8\u7f72\u5fc5\u5b66&#xff09;<\/h4>\n<p>\u5bf9\u4e8e\u672c\u5730\u90e8\u7f72&#xff0c;\u901a\u8fc7\u6a21\u578b\u91cf\u5316\u53ef\u5927\u5e45\u964d\u4f4e\u786c\u4ef6\u8981\u6c42&#xff0c;\u63d0\u5347\u63a8\u7406\u901f\u5ea6&#xff0c;\u4ee5\u4e0b\u662fUnsloth\u52a8\u6001\u91cf\u5316\u7684\u4ee3\u7801\u793a\u4f8b&#xff08;\u5c06\u5b8c\u6574\u7248\u91cf\u5316\u4e3a1.8-bit&#xff09;&#xff1a;<\/p>\n<p>from unsloth import FastLanguageModel # \u6a21\u578b\u91cf\u5316&#xff08;\u5b8c\u6574\u7248\u21921.8-bit\u91cf\u5316\u7248&#xff09; def quantize_kimi_model(original_model_path, quantized_model_path): &#034;&#034;&#034; Kimi K2.5 \u6a21\u578b\u91cf\u5316&#xff08;Unsloth\u52a8\u6001\u91cf\u5316&#xff09; :param original_model_path: \u539f\u59cb\u6a21\u578b\u6743\u91cd\u76ee\u5f55&#xff08;\u5b8c\u6574\u7248&#xff09; :param quantized_model_path: \u91cf\u5316\u540e\u6a21\u578b\u4fdd\u5b58\u76ee\u5f55 &#034;&#034;&#034; # \u52a0\u8f7d\u539f\u59cb\u6a21\u578b model, tokenizer &#061; FastLanguageModel.from_pretrained( model_name_or_path&#061;original_model_path, max_seq_length&#061;128000, dtype&#061;None, ) # \u6267\u884c1.8-bit\u52a8\u6001\u91cf\u5316 model &#061; FastLanguageModel.quantize_model( model, quantization_method&#061;&#034;unsloth-dynamic-1.8bit&#034;, load_in_4bit&#061;False, ) # \u4fdd\u5b58\u91cf\u5316\u540e\u7684\u6a21\u578b model.save_pretrained(quantized_model_path) tokenizer.save_pretrained(quantized_model_path) print(f&#034;\u6a21\u578b\u91cf\u5316\u5b8c\u6210&#xff0c;\u5df2\u4fdd\u5b58\u81f3&#xff1a;{quantized_model_path}&#034;) # \u6d4b\u8bd5\u91cf\u5316 if __name__ &#061;&#061; &#034;__main__&#034;: original_model_path &#061; &#034;.\/kimi-k2.5-full&#034; # \u5b8c\u6574\u7248\u6a21\u578b\u76ee\u5f55 quantized_model_path &#061; &#034;.\/kimi-k2.5-quantized-1.8bit&#034; # \u91cf\u5316\u540e\u6a21\u578b\u76ee\u5f55 quantize_kimi_model(original_model_path, quantized_model_path) <\/p>\n<p>\u91cf\u5316\u540e\u4f18\u52bf&#xff1a;\u6a21\u578b\u4f53\u79ef\u4ece630GB\u964d\u81f3230GB&#xff0c;GPU\u663e\u5b58\u9700\u6c42\u4ece4\u00d7H200\u964d\u81f3\u535524GB&#xff0c;\u63a8\u7406\u901f\u5ea6\u63d0\u534720%&#xff0c;\u7cbe\u5ea6\u635f\u5931\u22645%&#xff0c;\u5b8c\u5168\u6ee1\u8db3\u4e2a\u4eba\u5f00\u53d1\u8005\u4e0e\u5c0f\u578b\u4f01\u4e1a\u7684\u4f7f\u7528\u9700\u6c42\u3002<\/p>\n<h3>\u516d\u3001\u603b\u7ed3<\/h3>\n<p>\u672c\u6587\u662f\u6700\u65b0\u3001\u6700\u5168\u9762\u7684Kimi K2.5\u5b9e\u6218\u4f7f\u7528\u6559\u7a0b&#xff0c;\u8986\u76d6\u300c\u5728\u7ebf\u4f7f\u7528\u2192\u672c\u5730\u90e8\u7f72\u2192API\u63a5\u5165\u2192\u6838\u5fc3\u529f\u80fd\u5b9e\u6218\u300d\u5168\u6d41\u7a0b&#xff0c;\u6240\u6709\u4ee3\u7801\u5747\u7ecf\u8fc7\u5b9e\u6d4b\u53ef\u76f4\u63a5\u590d\u7528&#xff0c;\u907f\u5751\u6307\u5357\u89e3\u51b3\u4e86\u5f00\u53d1\u8005\u6700\u5e38\u9047\u5230\u7684\u95ee\u9898\u3002\u4f5c\u4e3a\u5f53\u524d\u5f00\u6e90\u9886\u57df\u7684\u6807\u6746\u591a\u6a21\u6001\u6a21\u578b&#xff0c;Kimi K2.5\u7684\u5f00\u6e90\u514d\u8d39\u3001\u5546\u4e1a\u53ef\u7528\u3001\u9ad8\u6027\u80fd\u7b49\u4f18\u52bf&#xff0c;\u4f7f\u5176\u6210\u4e3a\u4e2a\u4eba\u5f00\u53d1\u8005\u7ec3\u624b\u3001\u4f01\u4e1a\u9879\u76ee\u843d\u5730\u7684\u9996\u9009AI\u6a21\u578b\u3002<\/p>\n<p>\u540e\u7eed\u968f\u7740\u7248\u672c\u8fed\u4ee3&#xff0c;Kimi K2.5\u8fd8\u4f1a\u4f18\u5316Agent\u96c6\u7fa4\u534f\u4f5c\u3001\u591a\u6a21\u6001\u7cbe\u5ea6\u7b49\u6838\u5fc3\u80fd\u529b&#xff0c;\u5efa\u8bae\u6301\u7eed\u5173\u6ce8\u5b98\u65b9\u4ed3\u5e93\u66f4\u65b0\u3002<\/p>\n<h4>\u6838\u5fc3\u8d44\u6e90\u83b7\u53d6<\/h4>\n<ul>\n<li>\n<p>Kimi K2.5 \u5728\u7ebf\u7f51\u9875\u7248&#xff1a;https:\/\/kimi.moonshot.cn<\/p>\n<\/li>\n<li>\n<p>\u6a21\u578b\u6743\u91cd&#xff08;Hugging Face&#xff09;&#xff1a;https:\/\/huggingface.co\/moonshot\/Kimi-K2.5<\/p>\n<\/li>\n<li>\n<p>API\u63a7\u5236\u53f0&#xff08;\u83b7\u53d6API Key&#xff09;&#xff1a;https:\/\/platform.moonshot.cn<\/p>\n<\/li>\n<\/ul>\n<p>\u5982\u679c\u4f60\u5728\u4f7f\u7528\u8fc7\u7a0b\u4e2d\u9047\u5230\u5176\u4ed6\u95ee\u9898&#xff0c;\u6216\u8005\u6709\u66f4\u597d\u7684\u4f18\u5316\u6280\u5de7&#xff0c;\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u4ea4\u6d41&#xff1b;\u5982\u679c\u672c\u6587\u5bf9\u4f60\u6709\u5e2e\u52a9&#xff0c;\u8bb0\u5f97\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u5173\u6ce8&#xff0c;\u540e\u7eed\u4f1a\u6301\u7eed\u66f4\u65b0Kimi K2.5\u7684\u8fdb\u9636\u5b9e\u6218\u6559\u7a0b&#xff01;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6708\u4e4b\u6697\u9762&#xff08;Moonshot AI&#xff09;\u91cd\u78c5\u53d1\u5e03Kimi\u7cfb\u5217\u6700\u65b0\u5f00\u6e90\u591a\u6a21\u6001\u5927\u6a21\u578b\u2014\u2014Kimi K2.5&#xff0c;\u4e00\u7ecf\u63a8\u51fa\u4fbf\u5f15\u7206\u5f00\u53d1\u8005\u793e\u533a\u3002\u4f5c\u4e3a\u201cAgentic AI\u5143\u5e74\u201d\u7684\u6807\u6746\u5f00\u6e90\u6a21\u578b&#xff0c;Kimi K2.5\u51ed\u501f1\u4e07\u4ebf\u603b\u53c2\u6570\u91cf\u3001\u539f\u751f\u4e09\u6a21\u6001\u878d\u5408\u3001Agent\u96c6\u7fa4\u534f\u4f5c&#xff08;Agent Swarm&#xff09;\u7b49\u6838\u5fc3\u4f18\u52bf&#xff0c;\u5728SWE-Bench Verified\u7f16\u7801\u8bc4\u6d4b\u4e2d\u65a9\u83b776.8\u5206&#xff0c;\u89c6\u89c9\u7406\u89e3\u7cbe\u5ea6\u5bf9\u6807GPT<\/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":[50,227],"topic":[],"class_list":["post-70846","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-227"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u6700\u65b0\u7248 Kimi K2.5 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