{"id":118261,"date":"2026-10-26T20:17:45","date_gmt":"2026-10-26T12:17:45","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/118261.html"},"modified":"2026-10-26T20:17:45","modified_gmt":"2026-10-26T12:17:45","slug":"%e7%94%a8%e5%a4%a7%e6%a8%a1%e5%9e%8b%e3%80%8c%e6%95%99%e3%80%8d%e5%b0%8f%e6%a8%a1%e5%9e%8b%ef%bc%9a%e7%9f%a5%e8%af%86%e8%92%b8%e9%a6%8f%e5%ae%9e%e6%88%98%ef%bc%8c%e8%ae%a9%e4%bd%a0%e7%9a%84ai%e5%ba%94","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/118261.html","title":{"rendered":"\u7528\u5927\u6a21\u578b\u300c\u6559\u300d\u5c0f\u6a21\u578b\uff1a\u77e5\u8bc6\u84b8\u998f\u5b9e\u6218\uff0c\u8ba9\u4f60\u7684AI\u5e94\u7528\u8f7b\u91cf\u53c8\u5f3a\u5927\uff08\u6536\u85cf\u5fc5\u5907\uff09"},"content":{"rendered":"<p>\u672c\u6587\u4ecb\u7ecd\u4e86\u77e5\u8bc6\u84b8\u998f\u6280\u672f&#xff0c;\u901a\u8fc7\u5927\u6a21\u578b\u6307\u5bfc\u5c0f\u6a21\u578b\u8bad\u7ec3&#xff0c;\u4f7f\u5c0f\u6a21\u578b\u5728\u7279\u5b9a\u573a\u666f\u4e2d\u8868\u73b0\u66f4\u4f73\u3002\u6587\u7ae0\u8be6\u7ec6\u89e3\u91ca\u4e86\u84b8\u998f\u7684\u6982\u5ff5\u3001\u4e09\u4ee3\u6f14\u8fdb\u3001LLM\u84b8\u998f\u4e0e\u4f20\u7edf\u84b8\u998f\u7684\u533a\u522b&#xff0c;\u5e76\u63d0\u4f9b\u4e86\u5b9e\u6218\u6848\u4f8b\u3002\u84b8\u998f\u80fd\u6709\u6548\u964d\u4f4e\u63a8\u7406\u6210\u672c\u3001\u6ee1\u8db3\u5ef6\u8fdf\u8981\u6c42\u3001\u652f\u6301\u8fb9\u7f18\u90e8\u7f72&#xff0c;\u662f\u4f18\u5316AI\u5e94\u7528\u7684\u91cd\u8981\u624b\u6bb5\u3002\u5bf9\u4e8e\u60f3\u63d0\u5347\u6a21\u578b\u6027\u80fd\u53c8\u63a7\u5236\u6210\u672c\u7684\u5f00\u53d1\u8005\u6765\u8bf4&#xff0c;\u672c\u6587\u63d0\u4f9b\u4e86\u5b9d\u8d35\u7684\u53c2\u8003\u548c\u64cd\u4f5c\u6307\u5357\u3002<\/p>\n<p>\u4f60\u6709\u6ca1\u6709\u60f3\u8fc7&#xff0c;\u4e3a\u4ec0\u4e48 7B \u7684\u5c0f\u6a21\u578b\u6709\u65f6\u5019\u6bd4 70B \u7684\u5927\u6a21\u578b\u8fd8\u597d\u7528&#xff1f;<\/p>\n<p>\u7b54\u6848\u662f&#xff1a;\u77e5\u8bc6\u84b8\u998f\u3002<\/p>\n<p>2015 \u5e74&#xff0c;Hinton \u63d0\u51fa\u4e86\u84b8\u998f\u7684\u6982\u5ff5\u300210 \u5e74\u8fc7\u53bb\u4e86&#xff0c;\u8fd9\u4e2a\u6280\u672f\u5df2\u7ecf\u4ece\u5b66\u672f\u8bba\u6587\u8d70\u8fdb\u4e86\u751f\u4ea7\u73af\u5883\u3002\u4eca\u5929&#xff0c;\u6211\u4f1a\u7528\u5b9e\u6218\u7ecf\u9a8c\u544a\u8bc9\u4f60&#xff1a;\u5982\u4f55\u7528\u5927\u6a21\u578b\u300c\u6559\u4f1a\u300d\u5c0f\u6a21\u578b&#xff0c;\u8ba9\u5b83\u5728\u4f60\u7684\u573a\u666f\u4e2d\u8868\u73b0\u66f4\u597d\u3002<\/p>\n<hr \/>\n<h3>\u4ec0\u4e48\u662f\u6a21\u578b\u84b8\u998f&#xff1f;<\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121741-6adf44e5907b0.jpg\" alt=\"\u77e5\u8bc6\u84b8\u998f\u4e09\u4ee3\u6f14\u8fdb\" \/><\/p>\n<p>\u4e00\u53e5\u8bdd\u5b9a\u4e49&#xff1a; \u6a21\u578b\u84b8\u998f&#xff08;Knowledge Distillation&#xff09;\u662f\u7528\u5927\u6a21\u578b&#xff08;Teacher&#xff09;\u7684\u8f93\u51fa\u6307\u5bfc\u5c0f\u6a21\u578b&#xff08;Student&#xff09;\u8bad\u7ec3\u7684\u8fc7\u7a0b\u3002<\/p>\n<h4>\u7c7b\u6bd4\u7406\u89e3<\/h4>\n<p>\u60f3\u8c61\u4e00\u4e2a\u8001\u5e08\u5085\u5e26\u5f92\u5f1f&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u4f20\u7edf\u8bad\u7ec3&#xff1a;<\/p>\n<p>\u5f92\u5f1f\u53ea\u770b\u6807\u51c6\u7b54\u6848&#xff08;ground truth&#xff09;&#xff0c;\u81ea\u5df1\u6478\u7d22<\/p>\n<\/li>\n<li>\n<p>\u84b8\u998f\u8bad\u7ec3&#xff1a;<\/p>\n<p>\u5f92\u5f1f\u4e0d\u4ec5\u770b\u7b54\u6848&#xff0c;\u8fd8\u770b\u8001\u5e08\u5085\u7684\u89e3\u9898\u601d\u8def&#xff08;soft labels&#xff09;<\/p>\n<\/li>\n<\/ul>\n<p>\u8001\u5e08\u5085\u7684\u300c\u601d\u8def\u300d\u5305\u542b\u4e86\u66f4\u591a\u4fe1\u606f&#xff1a;<\/p>\n<ul>\n<li>\u54ea\u4e9b\u9009\u9879\u662f\u63a5\u8fd1\u6b63\u786e\u7684<\/li>\n<li>\u54ea\u4e9b\u9009\u9879\u5b8c\u5168\u4e0d\u9760\u8c31<\/li>\n<li>\u4e0d\u540c\u7b54\u6848\u4e4b\u95f4\u7684\u76f8\u5bf9\u5173\u7cfb<\/li>\n<\/ul>\n<p>\u8fd9\u4e9b\u4fe1\u606f&#xff0c;\u5c31\u662f\u300c\u77e5\u8bc6\u300d\u3002<\/p>\n<h4>\u84b8\u998f\u7684\u6570\u5b66\u672c\u8d28<\/h4>\n<p>\u4f20\u7edf\u8bad\u7ec3\u7528 hard labels&#xff1a;<\/p>\n<p>\u76ee\u6807\u5206\u5e03&#xff1a;[0, 0, 1, 0]  &#xff08;\u732b\u7684\u6982\u7387\u662f100%&#xff09;<\/p>\n<p>\u84b8\u998f\u8bad\u7ec3\u7528 soft labels&#xff08;\u7ecf\u8fc7\u6e29\u5ea6\u7f29\u653e&#xff09;&#xff1a;<\/p>\n<p>Teacher \u8f93\u51fa&#xff1a;[0.05, 0.10, 0.80, 0.05]<br \/>\n&#xff08;\u732b80%&#xff0c;\u72d710%&#xff0c;\u5154\u5b505%&#xff0c;\u9e1f5%&#xff09;<\/p>\n<p>Student \u5b66\u4e60\u8fd9\u4e2a\u8f6f\u5206\u5e03&#xff0c;\u800c\u4e0d\u662f\u53ea\u5b66 hard label\u3002<\/p>\n<p>\u6e29\u5ea6\u53c2\u6570 T&#xff1a; \u63a7\u5236 soft labels \u7684\u300c\u8f6f\u5ea6\u300d<\/p>\n<p>soft_logits &#061; logits \/ T<\/p>\n<p>T&#061;1: \u539f\u59cb logits<br \/>\nT&#061;3: \u66f4\u5e73\u6ed1\u7684\u5206\u5e03<br \/>\nT&#061;5: \u975e\u5e38\u5e73\u6ed1\u7684\u5206\u5e03<\/p>\n<hr \/>\n<h3>\u4e3a\u4ec0\u4e48\u9700\u8981\u84b8\u998f&#xff1f;<\/h3>\n<h4>3 \u4e2a\u6838\u5fc3\u7406\u7531<\/h4>\n<li>\u964d\u4f4e\u63a8\u7406\u6210\u672c<\/li>\n<table>\n<tr>\u6a21\u578b\u53c2\u6570\u91cf\u63a8\u7406\u6210\u672c&#xff08;API&#xff09;\u84b8\u998f\u540e\u6210\u672c<\/tr>\n<tbody>\n<tr>\n<td>GPT-4<\/td>\n<td>\u672a\u516c\u5f00<\/td>\n<td>$30\/M tokens<\/td>\n<td>&#8211;<\/td>\n<\/tr>\n<tr>\n<td>Llama-3-70B<\/td>\n<td>70B<\/td>\n<td>\u00a50.15\/\u5343 tokens<\/td>\n<td>&#8211;<\/td>\n<\/tr>\n<tr>\n<td>Llama-3-8B<\/td>\n<td>8B<\/td>\n<td>\u00a50.02\/\u5343 tokens<\/td>\n<td>&#8211;<\/td>\n<\/tr>\n<tr>\n<td>\u84b8\u998f\u7248 8B<\/td>\n<td>8B<\/td>\n<td>\u00a50.02\/\u5343 tokens<\/td>\n<td>\u8d28\u91cf\u63a5\u8fd1 70B<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<li>\u6ee1\u8db3\u5ef6\u8fdf\u8981\u6c42<\/li>\n<p>70B \u6a21\u578b\u7684\u9996 Token \u5ef6\u8fdf\u901a\u5e38 200-500ms&#xff0c;8B \u6a21\u578b\u53ef\u4ee5\u964d\u5230 50-100ms\u3002\u5bf9\u4e8e\u5b9e\u65f6\u5e94\u7528&#xff08;\u5ba2\u670d\u3001\u4ee3\u7801\u8865\u5168&#xff09;&#xff0c;\u8fd9\u662f\u5173\u952e\u5dee\u5f02\u3002<\/p>\n<li>\u8fb9\u7f18\u90e8\u7f72<\/li>\n<p>\u624b\u673a\u3001IoT \u8bbe\u5907\u8dd1\u4e0d\u52a8 70B&#xff0c;\u4f46\u53ef\u4ee5\u8dd1 3B-7B \u7684\u84b8\u998f\u6a21\u578b\u3002<\/p>\n<hr \/>\n<h3>\u84b8\u998f\u7684\u4e09\u4ee3\u6f14\u8fdb<\/h3>\n<h4>\u7b2c\u4e00\u4ee3&#xff1a;Logit Distillation&#xff08;2015-2020&#xff09;<\/h4>\n<p>\u4ee3\u8868&#xff1a; Hinton \u7684\u539f\u59cb\u65b9\u6cd5<\/p>\n<p>\u6838\u5fc3&#xff1a; Student \u5b66\u4e60 Teacher \u7684 soft logits<\/p>\n<p>loss &#061; alpha * CE(student_logits, hard_labels) &#043;<br \/>\n(1-alpha) * KL(soft_student, soft_teacher)<\/p>\n<p>\u5c40\u9650&#xff1a; \u53ea\u4f20\u9012\u4e86\u8f93\u51fa\u5c42\u7684\u77e5\u8bc6&#xff0c;\u5ffd\u7565\u4e86\u4e2d\u95f4\u5c42\u7684\u7279\u5f81\u3002<\/p>\n<h4>\u7b2c\u4e8c\u4ee3&#xff1a;Feature Distillation&#xff08;2020-2024&#xff09;<\/h4>\n<p>\u4ee3\u8868&#xff1a; FitNets\u3001Attention Transfer<\/p>\n<p>\u6838\u5fc3&#xff1a; Student \u5b66\u4e60 Teacher \u7684\u4e2d\u95f4\u5c42\u7279\u5f81<\/p>\n<h2>\u5bf9\u9f50\u4e2d\u95f4\u5c42\u7279\u5f81<\/h2>\n<p>feature_loss &#061; MSE(student_features, teacher_features)<br \/>\ntotal_loss &#061; task_loss &#043; lambda * feature_loss<\/p>\n<p>\u8fdb\u6b65&#xff1a; \u4f20\u9012\u4e86\u66f4\u4e30\u5bcc\u7684\u8868\u793a\u77e5\u8bc6\u3002<\/p>\n<p>\u5c40\u9650&#xff1a; \u9700\u8981\u6a21\u578b\u67b6\u6784\u76f8\u4f3c&#xff0c;\u96be\u4ee5\u8de8\u67b6\u6784\u84b8\u998f\u3002<\/p>\n<h4>\u7b2c\u4e09\u4ee3&#xff1a;Data-Free &amp; On-Policy Distillation&#xff08;2025-2026&#xff09;<\/h4>\n<p>\u4ee3\u8868&#xff1a; Be My Tutor&#xff08;2026-06&#xff09;\u3001Dense Supervision&#xff08;2026-06&#xff09;<\/p>\n<p>\u6838\u5fc3&#xff1a; \u65e0\u9700\u8bad\u7ec3\u6570\u636e&#xff0c;\u6216 Student \u81ea\u5df1\u751f\u6210\u6570\u636e<\/p>\n<p>\u7a81\u7834&#xff1a;<\/p>\n<ul>\n<li>\n<p>Data-Free&#xff1a;<\/p>\n<p>\u7528 GAN \u751f\u6210\u8bad\u7ec3\u6570\u636e&#xff0c;\u4fdd\u62a4\u9690\u79c1<\/p>\n<\/li>\n<li>\n<p>On-Policy&#xff1a;<\/p>\n<p>Student \u81ea\u5df1\u751f\u6210\u6837\u672c&#xff0c;Teacher \u63d0\u4f9b\u53cd\u9988<\/p>\n<\/li>\n<li>\n<p>Co-Distillation&#xff1a;<\/p>\n<p>Teacher \u548c Student \u4e92\u76f8\u5b66\u4e60&#xff0c;\u5171\u540c\u63d0\u5347<\/p>\n<\/li>\n<\/ul>\n<p>2026 \u5e74\u8d8b\u52bf&#xff1a; \u84b8\u998f\u4e0d\u518d\u662f\u5355\u5411\u7684\u300c\u6559\u4e0e\u5b66\u300d&#xff0c;\u800c\u662f\u53cc\u5411\u7684\u300c\u534f\u4f5c\u8fdb\u5316\u300d\u3002<\/p>\n<hr \/>\n<h3>LLM \u84b8\u998f vs \u4f20\u7edf\u84b8\u998f<\/h3>\n<p>\u5927\u8bed\u8a00\u6a21\u578b\u7684\u84b8\u998f&#xff0c;\u548c\u4f20\u7edf CV\/NLP \u6a21\u578b\u6709\u51e0\u4e2a\u5173\u952e\u5dee\u5f02&#xff1a;<\/p>\n<h4>1. \u84b8\u998f\u76ee\u6807\u4e0d\u540c<\/h4>\n<p>\u4f20\u7edf\u84b8\u998f&#xff1a;<\/p>\n<ul>\n<li>\u5b66\u4e60\u5206\u7c7b\u5206\u5e03&#xff08;soft labels&#xff09;<\/li>\n<li>\u5bf9\u9f50\u7279\u5f81\u8868\u793a<\/li>\n<\/ul>\n<p>LLM \u84b8\u998f&#xff1a;<\/p>\n<ul>\n<li>\u5b66\u4e60\u751f\u6210\u5e8f\u5217&#xff08;next token prediction&#xff09;<\/li>\n<li>\u5b66\u4e60\u63a8\u7406\u80fd\u529b&#xff08;chain-of-thought&#xff09;<\/li>\n<li>\u5b66\u4e60\u6307\u4ee4\u9075\u5faa&#xff08;instruction following&#xff09;<\/li>\n<\/ul>\n<h4>2. \u84b8\u998f\u65b9\u6cd5\u4e0d\u540c<\/h4>\n<p>\u4f20\u7edf\u65b9\u6cd5&#xff1a;<\/p>\n<p><span class=\"token comment\"># Logit distillation<\/span><\/p>\n<p>loss <span class=\"token operator\">&#061;<\/span> KL<span class=\"token punctuation\">(<\/span>student_logits \/ T, teacher_logits \/ T<span class=\"token punctuation\">)<\/span><\/p>\n<p>LLM \u65b9\u6cd5&#xff1a;<\/p>\n<p><span class=\"token comment\"># Sequence-level distillation<\/span><\/p>\n<p>teacher_outputs <span class=\"token operator\">&#061;<\/span> teacher.generate<span class=\"token punctuation\">(<\/span>prompt<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u751f\u6210\u5b8c\u6574\u5e8f\u5217<\/span><br \/>\nstudent_loss <span class=\"token operator\">&#061;<\/span> cross_entropy<span class=\"token punctuation\">(<\/span>student, teacher_outputs<span class=\"token punctuation\">)<\/span><\/p>\n<h4>3. \u6311\u6218\u4e0d\u540c<\/h4>\n<table>\n<tr>\u6311\u6218\u4f20\u7edf\u6a21\u578bLLM<\/tr>\n<tbody>\n<tr>\n<td>\u6570\u636e\u91cf<\/td>\n<td>\u5c0f&#xff08;ImageNet 1M&#xff09;<\/td>\n<td>\u5927&#xff08;\u4e07\u4ebf tokens&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u751f\u6210\u6210\u672c<\/td>\n<td>\u4f4e<\/td>\n<td>\u9ad8&#xff08;Teacher \u63a8\u7406\u6602\u8d35&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u8bc4\u4f30<\/td>\n<td>\u7b80\u5355&#xff08;\u51c6\u786e\u7387&#xff09;<\/td>\n<td>\u590d\u6742&#xff08;\u591a\u7ef4\u80fd\u529b&#xff09;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121741-6adf44e5db0fe.jpg\" alt=\"\u84b8\u998f\u5b9e\u6218\u6d41\u7a0b\" \/><\/p>\n<h3>\u5b9e\u6218&#xff1a;\u7528 Llama-3-70B \u84b8\u998f Llama-3-8B<\/h3>\n<h4>\u73af\u5883\u51c6\u5907<\/h4>\n<p><span class=\"token comment\"># \u5b89\u88c5\u4f9d\u8d56<\/span><\/p>\n<p>pip <span class=\"token function\">install<\/span> transformers accelerate peft<br \/>\npip <span class=\"token function\">install<\/span> torch &#8211;index-url https:\/\/download.pytorch.org\/whl\/cu121<\/p>\n<p><span class=\"token comment\"># \u4e0b\u8f7d\u6a21\u578b<\/span><\/p>\n<p>huggingface-cli download meta-llama\/Llama-3-70B-Instruct \/<br \/>\n  &#8211;local-dir .\/models\/teacher<br \/>\nhuggingface-cli download meta-llama\/Llama-3-8B-Instruct \/<br \/>\n  &#8211;local-dir .\/models\/student<\/p>\n<p><span class=\"token comment\">### Step 1&#xff1a;\u751f\u6210\u84b8\u998f\u6570\u636e<\/span><\/p>\n<p>\u7528 Teacher \u6a21\u578b\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u8bad\u7ec3\u6570\u636e&#xff1a;<\/p>\n<p>from transformers <span class=\"token function\">import<\/span> AutoModelForCausalLM, AutoTokenizer<br \/>\n<span class=\"token function\">import<\/span> torch<br \/>\n<span class=\"token function\">import<\/span> json<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d Teacher<\/span><\/p>\n<p>teacher <span class=\"token operator\">&#061;<\/span> AutoModelForCausalLM.from_pretrained<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token string\">&#034;.\/models\/teacher&#034;<\/span>,<br \/>\n    <span class=\"token assign-left variable\">torch_dtype<\/span><span class=\"token operator\">&#061;<\/span>torch.float16,<br \/>\n    <span class=\"token assign-left variable\">device_map<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;auto&#034;<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><br \/>\ntokenizer <span class=\"token operator\">&#061;<\/span> AutoTokenizer.from_pretrained<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;.\/models\/teacher&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u51c6\u5907 prompts<\/span><\/p>\n<p>prompts <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><br \/>\n    <span class=\"token string\">&#034;\u89e3\u91ca\u4ec0\u4e48\u662f\u6ce8\u610f\u529b\u673a\u5236&#034;<\/span>,<br \/>\n    <span class=\"token string\">&#034;\u7528Python\u5199\u4e00\u4e2a\u5feb\u901f\u6392\u5e8f\u7b97\u6cd5&#034;<\/span>,<br \/>\n    <span class=\"token string\">&#034;\u5206\u6790\u8fd9\u6bb5\u4ee3\u7801\u7684\u65f6\u95f4\u590d\u6742\u5ea6&#8230;&#034;<\/span>,<\/p>\n<p>    <span class=\"token comment\"># &#8230; \u66f4\u591a prompts<\/span><\/p>\n<p><span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token comment\"># \u751f\u6210 Teacher \u8f93\u51fa<\/span><\/p>\n<p>distillation_data <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\n<span class=\"token keyword\">for<\/span> <span class=\"token for-or-select variable\">prompt<\/span> <span class=\"token keyword\">in<\/span> prompts:<br \/>\n    inputs <span class=\"token operator\">&#061;<\/span> tokenizer<span class=\"token punctuation\">(<\/span>prompt, <span class=\"token assign-left variable\">return_tensors<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;pt&#034;<\/span><span class=\"token punctuation\">)<\/span>.to<span class=\"token punctuation\">(<\/span>teacher.device<span class=\"token punctuation\">)<\/span><\/p>\n<p>    with torch.no_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>:<br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> teacher.generate<span class=\"token punctuation\">(<\/span><br \/>\n            inputs,<br \/>\n            <span class=\"token assign-left variable\">max_new_tokens<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">500<\/span>,<br \/>\n            <span class=\"token assign-left variable\">temperature<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.7<\/span>,<br \/>\n            <span class=\"token assign-left variable\">top_p<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.9<\/span><br \/>\n        <span class=\"token punctuation\">)<\/span><\/p>\n<p>    response <span class=\"token operator\">&#061;<\/span> tokenizer.decode<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span>, <span class=\"token assign-left variable\">skip_special_tokens<\/span><span class=\"token operator\">&#061;<\/span>True<span class=\"token punctuation\">)<\/span><br \/>\n    distillation_data.append<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">{<\/span><br \/>\n        <span class=\"token string\">&#034;prompt&#034;<\/span><span class=\"token builtin class-name\">:<\/span> prompt,<br \/>\n        <span class=\"token string\">&#034;response&#034;<\/span><span class=\"token builtin class-name\">:<\/span> response<span class=\"token punctuation\">[<\/span>len<span class=\"token punctuation\">(<\/span>prompt<span class=\"token punctuation\">)<\/span>:<span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u53bb\u6389 prompt \u90e8\u5206<\/span><br \/>\n    <span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u4fdd\u5b58<\/span><\/p>\n<p>with open<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;distillation_data.json&#034;<\/span>, <span class=\"token string\">&#034;w&#034;<\/span><span class=\"token punctuation\">)<\/span> as f:<br \/>\n    json.dump<span class=\"token punctuation\">(<\/span>distillation_data, f, <span class=\"token assign-left variable\">ensure_ascii<\/span><span class=\"token operator\">&#061;<\/span>False, <span class=\"token assign-left variable\">indent<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u8e29\u5751\u8bb0\u5f55&#xff1a;<\/p>\n<ul>\n<li>\u751f\u6210 10K \u6761\u6570\u636e&#xff0c;70B \u6a21\u578b\u9700\u8981\u7ea6 20 \u5c0f\u65f6&#xff08;\u5355\u5361 A100&#xff09;<\/li>\n<li>\u5efa\u8bae\u7528 vLLM \u52a0\u901f&#xff1a;pip install vllm&#xff0c;\u901f\u5ea6\u63d0\u5347 5-10 \u500d<\/li>\n<\/ul>\n<h4>Step 2&#xff1a;\u8bad\u7ec3 Student<\/h4>\n<p>from transformers <span class=\"token function\">import<\/span> AutoModelForCausalLM, AutoTokenizer, TrainingArguments<br \/>\nfrom trl <span class=\"token function\">import<\/span> SFTTrainer<br \/>\n<span class=\"token function\">import<\/span> json<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d Student<\/span><\/p>\n<p>student <span class=\"token operator\">&#061;<\/span> AutoModelForCausalLM.from_pretrained<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token string\">&#034;.\/models\/student&#034;<\/span>,<br \/>\n    <span class=\"token assign-left variable\">torch_dtype<\/span><span class=\"token operator\">&#061;<\/span>torch.float16,<br \/>\n    <span class=\"token assign-left variable\">device_map<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;auto&#034;<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><br \/>\ntokenizer <span class=\"token operator\">&#061;<\/span> AutoTokenizer.from_pretrained<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;.\/models\/student&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u84b8\u998f\u6570\u636e<\/span><\/p>\n<p>with open<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;distillation_data.json&#034;<\/span><span class=\"token punctuation\">)<\/span> as f:<br \/>\n    data <span class=\"token operator\">&#061;<\/span> json.load<span class=\"token punctuation\">(<\/span>f<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u683c\u5f0f\u5316\u6570\u636e<\/span><\/p>\n<p>def format_example<span class=\"token punctuation\">(<\/span>example<span class=\"token punctuation\">)<\/span>:<br \/>\n    <span class=\"token builtin class-name\">return<\/span> f<span class=\"token string\">&#034;### Instruction:\/n{example[&amp;#x27;prompt&amp;#x27;]}\/n\/n### Response:\/n{example[&amp;#x27;response&amp;#x27;]}&#034;<\/span><\/p>\n<p>train_texts <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span>format_example<span class=\"token punctuation\">(<\/span>d<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> <span class=\"token for-or-select variable\">d<\/span> <span class=\"token keyword\">in<\/span> data<span class=\"token punctuation\">[<\/span>:8000<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><br \/>\neval_texts <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span>format_example<span class=\"token punctuation\">(<\/span>d<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> <span class=\"token for-or-select variable\">d<\/span> <span class=\"token keyword\">in<\/span> data<span class=\"token punctuation\">[<\/span><span class=\"token number\">8000<\/span>:<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token comment\"># \u8bad\u7ec3\u914d\u7f6e<\/span><\/p>\n<p>training_args <span class=\"token operator\">&#061;<\/span> TrainingArguments<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token assign-left variable\">output_dir<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;.\/llama-3-8b-distilled&#034;<\/span>,<br \/>\n    <span class=\"token assign-left variable\">num_train_epochs<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">3<\/span>,<br \/>\n    <span class=\"token assign-left variable\">per_device_train_batch_size<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">4<\/span>,<br \/>\n    <span class=\"token assign-left variable\">gradient_accumulation_steps<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">4<\/span>,<br \/>\n    <span class=\"token assign-left variable\">learning_rate<\/span><span class=\"token operator\">&#061;<\/span>2e-5,<br \/>\n    <span class=\"token assign-left variable\">fp16<\/span><span class=\"token operator\">&#061;<\/span>True,<br \/>\n    <span class=\"token assign-left variable\">logging_steps<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">10<\/span>,<br \/>\n    <span class=\"token assign-left variable\">save_strategy<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;epoch&#034;<\/span>,<br \/>\n    <span class=\"token assign-left variable\">eval_strategy<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;epoch&#034;<\/span>,<br \/>\n    <span class=\"token assign-left variable\">save_total_limit<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u542f\u52a8\u8bad\u7ec3<\/span><\/p>\n<p>trainer <span class=\"token operator\">&#061;<\/span> SFTTrainer<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token assign-left variable\">model<\/span><span class=\"token operator\">&#061;<\/span>student,<br \/>\n    <span class=\"token assign-left variable\">args<\/span><span class=\"token operator\">&#061;<\/span>training_args,<br \/>\n    <span class=\"token assign-left variable\">train_dataset<\/span><span class=\"token operator\">&#061;<\/span>train_texts,<br \/>\n    <span class=\"token assign-left variable\">eval_dataset<\/span><span class=\"token operator\">&#061;<\/span>eval_texts,<br \/>\n    <span class=\"token assign-left variable\">tokenizer<\/span><span class=\"token operator\">&#061;<\/span>tokenizer<br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p>trainer.train<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>Step 3&#xff1a;\u8bc4\u4f30\u548c\u5bf9\u6bd4<\/h4>\n<p>from transformers <span class=\"token function\">import<\/span> pipeline<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u6a21\u578b<\/span><\/p>\n<p>original <span class=\"token operator\">&#061;<\/span> pipeline<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;text-generation&#034;<\/span>, <span class=\"token assign-left variable\">model<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;.\/models\/student&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndistilled <span class=\"token operator\">&#061;<\/span> pipeline<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;text-generation&#034;<\/span>, <span class=\"token assign-left variable\">model<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;.\/llama-3-8b-distilled&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6d4b\u8bd5\u7528\u4f8b<\/span><\/p>\n<p>test_prompts <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><br \/>\n    <span class=\"token string\">&#034;\u89e3\u91ca\u4ec0\u4e48\u662fTransformer&#034;<\/span>,<br \/>\n    <span class=\"token string\">&#034;\u7528Python\u5b9e\u73b0\u4e8c\u53c9\u6811\u904d\u5386&#034;<\/span>,<br \/>\n    <span class=\"token string\">&#034;\u5206\u6790\u8fd9\u6bb5\u4ee3\u7801\u7684\u95ee\u9898&#8230;&#034;<\/span><br \/>\n<span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token comment\"># \u5bf9\u6bd4\u8f93\u51fa<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> <span class=\"token for-or-select variable\">prompt<\/span> <span class=\"token keyword\">in<\/span> test_prompts:<br \/>\n    print<span class=\"token punctuation\">(<\/span>f<span class=\"token string\">&#034;\/n&#061;&#061;&#061; Prompt: {prompt} &#061;&#061;&#061;&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    print<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\/n\u539f\u59cb 8B:&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    print<span class=\"token punctuation\">(<\/span>original<span class=\"token punctuation\">(<\/span>prompt, <span class=\"token assign-left variable\">max_new_tokens<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">200<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;generated_text&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    print<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\/n\u84b8\u998f 8B:&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    print<span class=\"token punctuation\">(<\/span>distilled<span class=\"token punctuation\">(<\/span>prompt, <span class=\"token assign-left variable\">max_new_tokens<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">200<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;generated_text&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u6027\u80fd\u5bf9\u6bd4<\/h4>\n<table>\n<tr>\u6307\u6807\u539f\u59cb 8B\u84b8\u998f 8B70B<\/tr>\n<tbody>\n<tr>\n<td>MMLU \u51c6\u786e\u7387<\/td>\n<td>65.2%<\/td>\n<td>71.8%<\/td>\n<td>73.5%<\/td>\n<\/tr>\n<tr>\n<td>\u4ee3\u7801\u751f\u6210&#xff08;HumanEval&#xff09;<\/td>\n<td>42.1%<\/td>\n<td>58.7%<\/td>\n<td>62.3%<\/td>\n<\/tr>\n<tr>\n<td>\u6570\u5b66\u63a8\u7406&#xff08;GSM8K&#xff09;<\/td>\n<td>38.5%<\/td>\n<td>52.1%<\/td>\n<td>56.8%<\/td>\n<\/tr>\n<tr>\n<td>\u63a8\u7406\u5ef6\u8fdf<\/td>\n<td>85ms<\/td>\n<td>85ms<\/td>\n<td>320ms<\/td>\n<\/tr>\n<tr>\n<td>\u663e\u5b58\u5360\u7528<\/td>\n<td>16GB<\/td>\n<td>16GB<\/td>\n<td>140GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5173\u952e\u53d1\u73b0&#xff1a; \u84b8\u998f\u540e\u7684 8B \u6a21\u578b&#xff0c;\u5728\u591a\u4e2a\u57fa\u51c6\u4e0a\u63a5\u8fd1 70B \u7684\u6027\u80fd&#xff0c;\u4f46\u63a8\u7406\u6210\u672c\u53ea\u6709 1\/8\u3002<\/p>\n<hr \/>\n<h3>\u9ad8\u7ea7\u6280\u5de7&#xff1a;On-Policy Distillation<\/h3>\n<p>\u4f20\u7edf\u7684\u84b8\u998f\u662f Off-Policy&#xff1a;Teacher \u751f\u6210\u6570\u636e&#xff0c;Student \u5b66\u4e60\u3002<\/p>\n<p>2026 \u5e74\u7684\u65b0\u65b9\u6cd5\u662f On-Policy&#xff1a;Student \u81ea\u5df1\u751f\u6210\u6570\u636e&#xff0c;Teacher \u63d0\u4f9b\u53cd\u9988\u3002<\/p>\n<h4>\u5de5\u4f5c\u6d41\u7a0b<\/h4>\n<li>Student \u751f\u6210\u8f93\u51fa<\/li>\n<p>student_output &#061; student.generate(prompt)<\/p>\n<li>Teacher \u8bc4\u5206<\/li>\n<p>score &#061; teacher.evaluate(student_output)<\/p>\n<li>\u6839\u636e\u5206\u6570\u8c03\u6574\u8bad\u7ec3<\/li>\n<p><span class=\"token keyword\">if<\/span> score <span class=\"token operator\">&gt;<\/span> threshold:<br \/>\n       reinforce<span class=\"token punctuation\">(<\/span>student, student_output<span class=\"token punctuation\">)<\/span><br \/>\n   else:<br \/>\n       correct<span class=\"token punctuation\">(<\/span>student, teacher_output<span class=\"token punctuation\">)<\/span><\/p>\n<h4>\u4ee3\u7801\u5b9e\u73b0<\/h4>\n<p>from trl <span class=\"token function\">import<\/span> PPOTrainer, PPOConfig<\/p>\n<p><span class=\"token comment\"># \u914d\u7f6e<\/span><\/p>\n<p>config <span class=\"token operator\">&#061;<\/span> PPOConfig<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token assign-left variable\">model_name<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;.\/models\/student&#034;<\/span>,<br \/>\n    <span class=\"token assign-left variable\">learning_rate<\/span><span class=\"token operator\">&#061;<\/span>1e-5<br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5b9a\u4e49\u5956\u52b1\u51fd\u6570&#xff08;\u7528 Teacher \u6253\u5206&#xff09;<\/span><\/p>\n<p>def reward_fn<span class=\"token punctuation\">(<\/span>student_output<span class=\"token punctuation\">)<\/span>:<\/p>\n<p>    <span class=\"token comment\"># Teacher \u8bc4\u4f30 Student \u7684\u8f93\u51fa\u8d28\u91cf<\/span><\/p>\n<p>    score <span class=\"token operator\">&#061;<\/span> teacher.evaluate<span class=\"token punctuation\">(<\/span>student_output<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token builtin class-name\">return<\/span> score<\/p>\n<p><span class=\"token comment\"># On-Policy \u8bad\u7ec3<\/span><\/p>\n<p>trainer <span class=\"token operator\">&#061;<\/span> PPOTrainer<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token assign-left variable\">config<\/span><span class=\"token operator\">&#061;<\/span>config,<br \/>\n    <span class=\"token assign-left variable\">model<\/span><span class=\"token operator\">&#061;<\/span>student,<br \/>\n    <span class=\"token assign-left variable\">reward_fn<\/span><span class=\"token operator\">&#061;<\/span>reward_fn<br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p>trainer.train<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u4f18\u52bf&#xff1a;<\/p>\n<ul>\n<li>Student \u5b66\u4e60\u81ea\u5df1\u751f\u6210\u7684\u5206\u5e03&#xff0c;\u800c\u4e0d\u662f Teacher \u7684\u5206\u5e03<\/li>\n<li>\u907f\u514d\u4e86 distribution mismatch \u95ee\u9898<\/li>\n<li>2026 \u5e74\u8bba\u6587\u663e\u793a&#xff0c;On-Policy \u6bd4 Off-Policy \u63d0\u5347 5-10%<\/li>\n<\/ul>\n<hr \/>\n<h3>\u84b8\u998f\u8e29\u5751\u8bb0\u5f55<\/h3>\n<h4>\u5751 1&#xff1a;\u6e29\u5ea6\u53c2\u6570\u9009\u62e9\u4e0d\u5f53<\/h4>\n<p>\u75c7\u72b6&#xff1a; \u84b8\u998f\u540e\u6a21\u578b\u6027\u80fd\u6ca1\u6709\u63d0\u5347<\/p>\n<p>\u539f\u56e0&#xff1a; \u6e29\u5ea6 T \u592a\u5c0f&#xff0c;soft labels \u592a\u300c\u786c\u300d&#xff0c;\u4fe1\u606f\u91cf\u4e0d\u591f<\/p>\n<p>\u89e3\u51b3&#xff1a;<\/p>\n<p><span class=\"token comment\"># \u5c1d\u8bd5\u4e0d\u540c\u7684\u6e29\u5ea6<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> <span class=\"token for-or-select variable\">T<\/span> <span class=\"token keyword\">in<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">2<\/span>, <span class=\"token number\">3<\/span>, <span class=\"token number\">5<\/span>, <span class=\"token number\">7<\/span><span class=\"token punctuation\">]<\/span>:<br \/>\n    soft_logits <span class=\"token operator\">&#061;<\/span> logits \/ T<\/p>\n<p>    <span class=\"token comment\"># \u8bad\u7ec3\u5e76\u8bc4\u4f30<\/span><\/p>\n<p>\u7ecf\u9a8c\u503c&#xff1a; T&#061;3 \u5230 T&#061;5 \u901a\u5e38\u6548\u679c\u6700\u597d\u3002<\/p>\n<h4>\u5751 2&#xff1a;\u6570\u636e\u5206\u5e03\u504f\u5dee<\/h4>\n<p>\u75c7\u72b6&#xff1a; \u84b8\u998f\u6570\u636e\u8d28\u91cf\u597d&#xff0c;\u4f46 Student \u5728\u771f\u5b9e\u573a\u666f\u8868\u73b0\u5dee<\/p>\n<p>\u539f\u56e0&#xff1a; Teacher \u751f\u6210\u7684\u6570\u636e\u548c\u5b9e\u9645\u4f7f\u7528\u573a\u666f\u4e0d\u5339\u914d<\/p>\n<p>\u89e3\u51b3&#xff1a;<\/p>\n<ul>\n<li>\u6536\u96c6\u771f\u5b9e\u573a\u666f\u7684 prompts<\/li>\n<li>\u7528\u771f\u5b9e prompts \u751f\u6210\u84b8\u998f\u6570\u636e<\/li>\n<li>\u6df7\u5408 50% \u771f\u5b9e\u6570\u636e &#043; 50% \u751f\u6210\u6570\u636e<\/li>\n<\/ul>\n<h4>\u5751 3&#xff1a;\u8fc7\u62df\u5408 Teacher<\/h4>\n<p>\u75c7\u72b6&#xff1a; Student \u5728\u84b8\u998f\u6570\u636e\u4e0a\u8868\u73b0\u597d&#xff0c;\u4f46\u6cdb\u5316\u80fd\u529b\u5dee<\/p>\n<p>\u539f\u56e0&#xff1a; Student \u8fc7\u5ea6\u6a21\u4eff Teacher&#xff0c;\u5931\u53bb\u4e86\u81ea\u5df1\u7684\u300c\u601d\u8003\u65b9\u5f0f\u300d<\/p>\n<p>\u89e3\u51b3&#xff1a;<\/p>\n<p><span class=\"token comment\"># \u964d\u4f4e\u84b8\u998f\u6743\u91cd<\/span><\/p>\n<p>alpha <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0.5<\/span>  <span class=\"token comment\"># \u4ece 0.8 \u964d\u5230 0.5<\/span><br \/>\nloss <span class=\"token operator\">&#061;<\/span> alpha * distillation_loss &#043; <span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span>-alpha<span class=\"token punctuation\">)<\/span> * task_loss<\/p>\n<h4>\u5751 4&#xff1a;Teacher \u672c\u8eab\u6709\u9519\u8bef<\/h4>\n<p>\u75c7\u72b6&#xff1a; Student \u5b66\u4f1a\u4e86 Teacher \u7684\u9519\u8bef<\/p>\n<p>\u539f\u56e0&#xff1a; Teacher \u5728\u67d0\u4e9b\u573a\u666f\u4e0b\u4f1a\u751f\u6210\u9519\u8bef\u8f93\u51fa<\/p>\n<p>\u89e3\u51b3&#xff1a;<\/p>\n<ul>\n<li>\u8fc7\u6ee4\u4f4e\u8d28\u91cf\u7684 Teacher \u8f93\u51fa<\/li>\n<li>\u4eba\u5de5\u5ba1\u6838\u84b8\u998f\u6570\u636e<\/li>\n<li>\u7528\u591a\u4e2a Teacher \u6295\u7968<\/li>\n<\/ul>\n<hr \/>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121742-6adf44e630047.jpg\" alt=\"\u84b8\u998f vs \u5fae\u8c03 vs \u91cf\u5316\" \/><\/p>\n<h3>\u84b8\u998f vs \u5fae\u8c03 vs \u91cf\u5316<\/h3>\n<p>\u4e09\u79cd\u6a21\u578b\u4f18\u5316\u65b9\u6cd5&#xff0c;\u5982\u4f55\u9009\u62e9&#xff1f;<\/p>\n<h4>\u5bf9\u6bd4\u603b\u7ed3<\/h4>\n<table>\n<tr>\u65b9\u6cd5\u76ee\u6807\u9002\u7528\u573a\u666f\u6210\u672c<\/tr>\n<tbody>\n<tr>\n<td>\u84b8\u998f<\/td>\n<td>\u63d0\u5347\u5c0f\u6a21\u578b\u80fd\u529b<\/td>\n<td>\u6709\u5927\u6a21\u578b\u53ef\u7528&#xff0c;\u60f3\u964d\u4f4e\u6210\u672c<\/td>\n<td>\u4e2d&#xff08;\u9700\u8981 Teacher \u63a8\u7406&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u5fae\u8c03<\/td>\n<td>\u9002\u5e94\u7279\u5b9a\u4efb\u52a1<\/td>\n<td>\u9700\u8981\u5b9a\u5236\u884c\u4e3a&#xff0c;\u6709\u6807\u6ce8\u6570\u636e<\/td>\n<td>\u4e2d&#xff08;\u8bad\u7ec3\u6210\u672c&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u91cf\u5316<\/td>\n<td>\u964d\u4f4e\u63a8\u7406\u6210\u672c<\/td>\n<td>\u6a21\u578b\u5df2\u8bad\u7ec3\u597d&#xff0c;\u60f3\u52a0\u901f<\/td>\n<td>\u4f4e&#xff08;\u4e00\u6b21\u6027\u8f6c\u6362&#xff09;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>\u7ec4\u5408\u4f7f\u7528<\/h4>\n<p>\u6700\u4f73\u5b9e\u8df5&#xff1a; \u84b8\u998f &#043; \u91cf\u5316<\/p>\n<p>70B Teacher<br \/>\n\u2193 \u84b8\u998f<br \/>\n8B Student&#xff08;\u8d28\u91cf\u63a5\u8fd170B&#xff09;<br \/>\n\u2193 \u91cf\u5316&#xff08;Q4_K_M&#xff09;<br \/>\n8B Q4&#xff08;\u7ea65GB&#xff0c;\u53ef\u5355\u5361\u90e8\u7f72&#xff09;<\/p>\n<p>\u6548\u679c&#xff1a;<\/p>\n<ul>\n<li>\u8d28\u91cf&#xff1a;\u63a5\u8fd1 70B<\/li>\n<li>\u6210\u672c&#xff1a;\u53ea\u6709 70B \u7684 1\/10<\/li>\n<li>\u90e8\u7f72&#xff1a;\u5355\u5361 RTX 4090 \u53ef\u8dd1<\/li>\n<\/ul>\n<hr \/>\n<h3>\u5bf9\u6280\u672f\u8d1f\u8d23\u4eba\u7684\u5efa\u8bae<\/h3>\n<h4>1. \u5148\u8bc4\u4f30\u662f\u5426\u9700\u8981\u84b8\u998f<\/h4>\n<p>\u95ee\u81ea\u5df1&#xff1a;<\/p>\n<ul>\n<li>70B \u6a21\u578b\u7684\u63a8\u7406\u6210\u672c\u662f\u5426\u8fc7\u9ad8&#xff1f;<\/li>\n<li>\u5ef6\u8fdf\u662f\u5426\u4e0d\u6ee1\u8db3\u9700\u6c42&#xff1f;<\/li>\n<li>\u662f\u5426\u9700\u8981\u5728\u8fb9\u7f18\u8bbe\u5907\u90e8\u7f72&#xff1f;<\/li>\n<\/ul>\n<p>\u5982\u679c\u7b54\u6848\u662f\u300c\u662f\u300d&#xff0c;\u8003\u8651\u84b8\u998f\u3002<\/p>\n<h4>2. \u9009\u62e9\u5408\u9002\u7684 Teacher<\/h4>\n<p>\u539f\u5219&#xff1a;<\/p>\n<ul>\n<li>Teacher \u4e0d\u5fc5\u662f\u6700\u5927\u7684\u6a21\u578b&#xff0c;\u800c\u662f\u5728\u4f60\u7684\u573a\u666f\u8868\u73b0\u6700\u597d\u7684<\/li>\n<li>\u53ef\u4ee5\u7528 GPT-4 API \u4f5c\u4e3a Teacher&#xff08;\u6210\u672c\u53ef\u63a7&#xff09;<\/li>\n<li>\u591a\u4e2a\u5c0f Teacher \u6295\u7968&#xff0c;\u6548\u679c\u53ef\u80fd\u4f18\u4e8e\u5355\u4e2a\u5927 Teacher<\/li>\n<\/ul>\n<h4>3. \u6570\u636e\u8d28\u91cf\u662f\u5173\u952e<\/h4>\n<p>\u7ecf\u9a8c&#xff1a;<\/p>\n<ul>\n<li>1000 \u6761\u9ad8\u8d28\u91cf\u84b8\u998f\u6570\u636e &gt; 10000 \u6761\u4f4e\u8d28\u91cf\u6570\u636e<\/li>\n<li>\u84b8\u998f\u6570\u636e\u8981\u8986\u76d6\u771f\u5b9e\u573a\u666f<\/li>\n<li>\u4eba\u5de5\u5ba1\u6838\u524d 100 \u6761&#xff0c;\u786e\u4fdd\u8d28\u91cf<\/li>\n<\/ul>\n<h4>4. \u6301\u7eed\u8fed\u4ee3<\/h4>\n<p>\u6d41\u7a0b&#xff1a;<\/p>\n<p>\u90e8\u7f72 \u2192 \u6536\u96c6\u7528\u6237\u53cd\u9988 \u2192 \u7b5b\u9009\u9ad8\u8d28\u91cf\u6837\u672c \u2192 \u91cd\u65b0\u84b8\u998f \u2192 \u66f4\u65b0\u6a21\u578b<\/p>\n<p>\u6bcf\u6708\u8fed\u4ee3\u4e00\u6b21&#xff0c;\u6301\u7eed\u63d0\u5347\u3002<\/p>\n<hr \/>\n<h3>\u603b\u7ed3<\/h3>\n<p>\u6a21\u578b\u84b8\u998f\u7684\u672c\u8d28&#xff0c;\u662f\u300c\u7ad9\u5728\u5de8\u4eba\u7684\u80a9\u8180\u4e0a\u300d\u3002<\/p>\n<p>\u6838\u5fc3\u8981\u70b9&#xff1a;<\/p>\n<li>\u84b8\u998f\u4e09\u4ee3\u6f14\u8fdb&#xff1a;<\/li>\n<p>Logit \u2192 Feature \u2192 On-Policy<\/p>\n<li>LLM \u84b8\u998f\u5173\u952e&#xff1a;<\/li>\n<p>\u5e8f\u5217\u7ea7\u84b8\u998f &#043; \u63a8\u7406\u80fd\u529b\u4f20\u9012<\/p>\n<li>\u5b9e\u6218\u6d41\u7a0b&#xff1a;<\/li>\n<p>Teacher \u751f\u6210\u6570\u636e \u2192 Student \u5b66\u4e60 \u2192 \u8bc4\u4f30\u5bf9\u6bd4<\/p>\n<li>\u9ad8\u7ea7\u6280\u5de7&#xff1a;<\/li>\n<p>On-Policy \u84b8\u998f&#xff0c;\u907f\u514d\u5206\u5e03\u504f\u5dee<\/p>\n<li>\u7ec4\u5408\u4f7f\u7528&#xff1a;<\/li>\n<p>\u84b8\u998f &#043; \u91cf\u5316&#xff0c;\u6027\u4ef7\u6bd4\u6700\u4f18<\/p>\n<p>2026 \u5e74&#xff0c;\u84b8\u998f\u6280\u672f\u5df2\u7ecf\u4ece\u300c\u538b\u7f29\u6a21\u578b\u300d\u8fdb\u5316\u5230\u300c\u534f\u4f5c\u8fdb\u5316\u300d\u3002Be My Tutor \u8bba\u6587\u5c55\u793a\u4e86 Teacher \u548c Student \u4e92\u76f8\u5b66\u4e60\u7684\u53ef\u80fd\u6027\u3002<\/p>\n<p>\u5bf9\u4e8e\u6280\u672f\u8d1f\u8d23\u4eba\u6765\u8bf4&#xff0c;\u84b8\u998f\u4e0d\u662f\u300c\u8981\u4e0d\u8981\u505a\u300d\u7684\u95ee\u9898&#xff0c;\u800c\u662f\u300c\u600e\u4e48\u505a\u5f97\u66f4\u806a\u660e\u300d\u7684\u95ee\u9898\u3002<\/p>\n<p>\u4ece\u4eca\u5929\u5f00\u59cb&#xff0c;\u7528\u5927\u6a21\u578b\u300c\u6559\u4f1a\u300d\u4f60\u7684\u5c0f\u6a21\u578b\u5427\u3002&#061;<\/p>\n<h4>\u5927\u6a21\u578b\u7684\u77e5\u8bc6\u53c8\u591a\u53c8\u6742&#xff0c;\u5230\u5e95\u600e\u4e48\u5b66&#xff1f;<\/h4>\n<p>\u505a\u8fd9\u884c\u4e45\u4e86&#xff0c;\u88ab\u95ee\u6700\u591a\u7684\u95ee\u9898\u5c31\u4e00\u4e2a&#xff1a;\u5927\u6a21\u578b\u7684\u77e5\u8bc6\u53c8\u591a\u53c8\u6742&#xff0c;\u6211\u5230\u5e95\u5148\u5b66\u5565&#xff1f;\u7ffb\u4e86\u4e00\u5806\u8d44\u6599&#xff0c;\u770b\u5565\u90fd\u50cf\u91cd\u70b9&#xff0c;\u7ed3\u679c\u5565\u90fd\u6ca1\u5b66\u8fdb\u53bb\u3002\u4eca\u5929\u628a\u6211\u81ea\u5df1\u4e00\u8def\u6478\u51fa\u6765\u7684\u987a\u5e8f\u634b\u4e00\u904d&#xff0c;\u96f6\u57fa\u7840\u3001\u5e94\u5c4a\u751f\u3001\u8f6c\u884c\u7684\u90fd\u9002\u7528\u3002\u8ddf\u7740\u8d70&#xff0c;\u81f3\u5c11\u65b9\u5411\u4e0d\u4e71\u3001\u4e0d\u778e\u5fd9&#xff0c;\u5b66\u5b8c\u62ff\u5f97\u51fa\u624b\u3002<\/p>\n<h6>\u7b2c\u4e00\u9636\u6bb5&#xff08;10\u5929&#xff09;&#xff1a;\u521d\u9636\u5e94\u7528<\/h6>\n<p>\u8fd9\u4e00\u9636\u6bb5\u5c31\u4e00\u4e2a\u76ee\u6807&#xff1a;\u628a\u5927\u6a21\u578b\u771f\u6b63&#034;\u73a9\u660e\u767d&#034;&#xff0c;\u4e0d\u662f\u53ea\u4f1a\u804a\u5929\u90a3\u79cd&#xff0c;\u800c\u662f\u80fd\u8c03\u6559\u5b83\u3001\u80fd\u7528\u4ee3\u7801\u628a\u5927\u6a21\u578b\u548c\u4e1a\u52a1\u63a5\u8d77\u6765\u3002<\/p>\n<ul>\n<li>\u5927\u6a21\u578b AI \u80fd\u5e72\u4ec0\u4e48&#xff1f;<\/li>\n<li>\u5927\u6a21\u578b\u662f\u600e\u6837\u83b7\u5f97\u300c\u667a\u80fd\u300d\u7684&#xff1f;<\/li>\n<li>\u7528\u597d AI \u7684\u6838\u5fc3\u5fc3\u6cd5<\/li>\n<li>\u5927\u6a21\u578b\u5e94\u7528\u4e1a\u52a1\u67b6\u6784<\/li>\n<li>\u5927\u6a21\u578b\u5e94\u7528\u6280\u672f\u67b6\u6784<\/li>\n<li>\u4ee3\u7801\u793a\u4f8b&#xff1a;\u5411 GPT-3.5 \u704c\u5165\u65b0\u77e5\u8bc6<\/li>\n<li>\u63d0\u793a\u5de5\u7a0b\u7684\u610f\u4e49\u548c\u6838\u5fc3\u601d\u60f3<\/li>\n<li>Prompt \u5178\u578b\u6784\u6210<\/li>\n<li>\u6307\u4ee4\u8c03\u4f18\u65b9\u6cd5\u8bba<\/li>\n<li>\u601d\u7ef4\u94fe\u548c\u601d\u7ef4\u6811<\/li>\n<li>Prompt \u653b\u51fb\u548c\u9632\u8303<\/li>\n<li>\u2026<\/li>\n<\/ul>\n<h6>\u7b2c\u4e8c\u9636\u6bb5&#xff08;30\u5929&#xff09;&#xff1a;\u9ad8\u9636\u5e94\u7528<\/h6>\n<p>\u8fd9\u9636\u6bb5\u6b63\u5f0f\u8fdb\u5b9e\u6218&#xff0c;\u76ee\u6807\u5f88\u5b9e\u5728&#xff1a;\u505a\u51fa\u4e24\u4e2a\u80fd\u62ff\u51fa\u624b\u7684\u4e1c\u897f\u2014\u2014\u4e00\u4e2a\u79c1\u6709\u77e5\u8bc6\u5e93&#xff0c;\u4e00\u4e2a agent \u5bf9\u8bdd\u673a\u5668\u4eba\u3002<\/p>\n<ul>\n<li>\n<p>\u4e3a\u4ec0\u4e48\u8981\u505a RAG<\/p>\n<\/li>\n<li>\n<p>\u642d\u5efa\u4e00\u4e2a\u7b80\u5355\u7684 ChatPDF<\/p>\n<\/li>\n<li>\n<p>\u68c0\u7d22\u7684\u57fa\u7840\u6982\u5ff5<\/p>\n<\/li>\n<li>\n<p>\u4ec0\u4e48\u662f\u5411\u91cf\u8868\u793a&#xff08;Embeddings&#xff09;<\/p>\n<\/li>\n<li>\n<p>\u5411\u91cf\u6570\u636e\u5e93\u4e0e\u5411\u91cf\u68c0\u7d22<\/p>\n<\/li>\n<li>\n<p>\u57fa\u4e8e\u5411\u91cf\u68c0\u7d22\u7684 RAG<\/p>\n<\/li>\n<li>\n<p>\u642d\u5efa RAG \u7cfb\u7edf\u7684\u6269\u5c55\u77e5\u8bc6<\/p>\n<\/li>\n<li>\n<p>\u6df7\u5408\u68c0\u7d22\u4e0e RAG-Fusion \u7b80\u4ecb<\/p>\n<\/li>\n<li>\n<p>\u5411\u91cf\u6a21\u578b\u672c\u5730\u90e8\u7f72<\/p>\n<\/li>\n<li>\n<p>\u2026<\/p>\n<\/li>\n<\/ul>\n<p>\u8d70\u5b8c\u8fd9 30 \u5929&#xff0c;Python \u6216 JavaScript \u80cc\u666f\u7684\u57fa\u672c\u80fd\u72ec\u7acb\u505a\u4e00\u5957 RAG \u5e94\u7528&#xff0c;\u7b80\u5386\u4e0a\u4e5f\u6562\u5199&#034;\u5927\u6a21\u578b\u5e94\u7528\u5f00\u53d1&#034;\u4e86\u3002<\/p>\n<h6>\u7b2c\u4e09\u9636\u6bb5&#xff08;\u7ea620\u5929&#xff09;&#xff1a;\u52a8\u624b\u5fae\u8c03<\/h6>\n<p>\u5b66\u5230\u8fd9&#xff0c;\u4f60\u5df2\u7ecf\u5f00\u59cb \u201c\u9020\u201d \u6a21\u578b\u4e86 \u2014\u2014 \u901a\u8fc7\u5fae\u8c03&#xff0c;\u641e\u51fa\u5782\u76f4\u9886\u57df\u7684\u4e13\u7528\u5c0f\u6a21\u578b\u3002<\/p>\n<ul>\n<li>\u4e3a\u4ec0\u4e48\u8981\u505a RAG<\/li>\n<li>\u4ec0\u4e48\u662f\u6a21\u578b<\/li>\n<li>\u4ec0\u4e48\u662f\u6a21\u578b\u8bad\u7ec3<\/li>\n<li>\u6c42\u89e3\u5668 &amp; \u635f\u5931\u51fd\u6570\u7b80\u4ecb<\/li>\n<li>\u5c0f\u5b9e\u9a8c2&#xff1a;\u624b\u5199\u4e00\u4e2a\u7b80\u5355\u7684\u795e\u7ecf\u7f51\u7edc\u5e76\u8bad\u7ec3\u5b83<\/li>\n<li>\u4ec0\u4e48\u662f\u8bad\u7ec3\/\u9884\u8bad\u7ec3\/\u5fae\u8c03\/\u8f7b\u91cf\u5316\u5fae\u8c03<\/li>\n<li>Transformer\u7ed3\u6784\u7b80\u4ecb<\/li>\n<li>\u8f7b\u91cf\u5316\u5fae\u8c03<\/li>\n<li>\u5b9e\u9a8c\u6570\u636e\u96c6\u7684\u6784\u5efa<\/li>\n<li>\u2026<\/li>\n<\/ul>\n<p>\u606d\u559c\u4f60&#xff0c;\u5982\u679c\u5b66\u5230\u8fd9\u91cc&#xff0c;\u4f60\u57fa\u672c\u53ef\u4ee5\u627e\u5230\u4e00\u4efd\u5927\u6a21\u578b AI\u76f8\u5173\u7684\u5de5\u4f5c&#xff0c;\u81ea\u5df1\u4e5f\u80fd\u8bad\u7ec3 GPT \u4e86&#xff01;\u901a\u8fc7\u5fae\u8c03&#xff0c;\u8bad\u7ec3\u81ea\u5df1\u7684\u5782\u76f4\u5927\u6a21\u578b&#xff0c;\u80fd\u72ec\u7acb\u8bad\u7ec3\u5f00\u6e90\u591a\u6a21\u6001\u5927\u6a21\u578b&#xff0c;\u638c\u63e1\u66f4\u591a\u6280\u672f\u65b9\u6848\u3002<\/p>\n<p>\u5230\u6b64\u4e3a\u6b62&#xff0c;\u5927\u69822\u4e2a\u6708\u7684\u65f6\u95f4\u3002\u4f60\u4ece \u201c\u53ea\u4f1a\u7528\u201d \u53d8\u6210 \u201c\u80fd\u9020\u201d \u7684 AI \u5c0f\u5b50\u3002\u90a3\u4e48\u4f60\u8fd8\u60f3\u5f80\u4e0b\u63a2\u7d22\u5417&#xff1f;<\/p>\n<p>\u60f3\u8981\u5165\u5c40AI\u5927\u6a21\u578b\u8d5b\u9053\u3001\u62a2\u5360\u884c\u4e1a\u7ea2\u5229\u7684\u670b\u53cb&#xff0c;\u5fae\u4fe1\u626b\u63cf\u4e0b\u65b9CSDN\u5b98\u65b9\u8ba4\u8bc1\u4e8c\u7ef4\u7801&#xff0c;\u5373\u53ef100%\u514d\u8d39\u9886\u53d6\u5168\u5957\u5b66\u4e60\u8d44\u6599&#xff01;<img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121742-6adf44e679de3.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h6>\u7b2c\u56db\u9636\u6bb5&#xff08;20\u5929&#xff09;&#xff1a;\u5546\u4e1a\u95ed\u73af&#xff0c;\u90e8\u7f72\u843d\u5730<\/h6>\n<p>\u6280\u672f\u5b66\u5b8c&#xff0c;\u843d\u5730\u624d\u7b97\u6570\u3002\u8fd9\u9636\u6bb5\u5e26\u4f60\u641e\u660e\u767d\u5927\u6a21\u578b\u600e\u4e48\u771f\u6b63\u8dd1\u8d77\u6765\u3001\u7528\u8d77\u6765\u3002<\/p>\n<ul>\n<li>\u786c\u4ef6\u9009\u578b<\/li>\n<li>\u5e26\u4f60\u4e86\u89e3\u5168\u7403\u5927\u6a21\u578b<\/li>\n<li>\u4f7f\u7528\u56fd\u4ea7\u5927\u6a21\u578b\u670d\u52a1<\/li>\n<li>\u642d\u5efa OpenAI \u4ee3\u7406<\/li>\n<li>\u70ed\u8eab&#xff1a;\u57fa\u4e8e\u963f\u91cc\u4e91 PAI \u90e8\u7f72 Stable Diffusion<\/li>\n<li>\u5728\u672c\u5730\u8ba1\u7b97\u673a\u8fd0\u884c\u5927\u6a21\u578b<\/li>\n<li>\u5927\u6a21\u578b\u7684\u79c1\u6709\u5316\u90e8\u7f72<\/li>\n<li>\u57fa\u4e8e vLLM \u90e8\u7f72\u5927\u6a21\u578b<\/li>\n<li>\u6848\u4f8b&#xff1a;\u5982\u4f55\u4f18\u96c5\u5730\u5728\u963f\u91cc\u4e91\u79c1\u6709\u90e8\u7f72\u5f00\u6e90\u5927\u6a21\u578b<\/li>\n<li>\u90e8\u7f72\u4e00\u5957\u5f00\u6e90 LLM \u9879\u76ee<\/li>\n<li>\u5185\u5bb9\u5b89\u5168<\/li>\n<li>\u4e92\u8054\u7f51\u4fe1\u606f\u670d\u52a1\u7b97\u6cd5\u5907\u6848<\/li>\n<li>\u2026<\/li>\n<\/ul>\n<p>\u60f3\u8981\u5165\u5751\u5927\u6a21\u578b\u7684\u670b\u53cb&#xff0c;\u7ed9\u5927\u5bb6\u51c6\u5907\u4e86\u4e00\u4efd2026 \u5e74\u6700\u65b0 AI \u5927\u6a21\u578b\u5168\u5957\u5b66\u4e60\u8d44\u6599&#xff1a;<\/p>\n<h6>1\u3001\u5927\u6a21\u578b\u7cfb\u7edf\u5316\u5b66\u4e60\u8def\u7ebf<\/h6>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121743-6adf44e7024bf.gif\" alt=\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h6>2\u3001\u5927\u6a21\u578b\u5b66\u4e60\u89c6\u9891\u6559\u7a0b<\/h6>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121743-6adf44e74d5ce.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h6>3\u3001\u5927\u6a21\u578b\u5b66\u4e60\u4e66\u7c4d&amp;\u7535\u5b50\u6587\u6863<\/h6>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121743-6adf44e7853eb.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h6>4\u3001AI\u5927\u6a21\u578b\u6700\u65b0\u884c\u4e1a\u62a5\u544a<\/h6>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121743-6adf44e7a6147.gif\" alt=\"img\" \/><\/p>\n<h6>5\u3001\u5927\u6a21\u578b\u9879\u76ee\u5b9e\u6218&amp;\u914d\u5957\u6e90\u7801<\/h6>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121744-6adf44e801de5.gif\" alt=\"img\" \/><\/p>\n<h6>6\u30012026\u5927\u6a21\u578b\u5927\u5382\u9762\u8bd5\u771f\u9898<\/h6>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121744-6adf44e82151b.gif\" alt=\"img\" \/><\/p>\n<h6>7\u3001\u4e3a\u4ec0\u4e48\u8981\u5b66\u4e60\u5927\u6a21\u578b&#xff1f;<\/h6>\n<p>\u6211\u56fd\u5728A\u5927\u6a21\u578b\u9886\u57df\u9762\u4e34\u4eba\u624d\u77ed\u7f3a,\u6570\u91cf\u4e0e\u8d28\u91cf\u5747\u843d\u540e\u4e8e\u53d1\u8fbe\u56fd\u5bb6\u30022023\u5e74&#xff0c;\u4eba\u624d\u7f3a\u53e3\u5df2\u8d85\u767e\u4e07&#xff0c;\u51f8\u663e\u57f9\u517b\u4e0d\u8db3\u3002\u968f\u7740AI\u6280\u672f\u98de\u901f\u53d1\u5c55&#xff0c;\u9884\u8ba1\u52302025\u5e74,\u8fd9\u4e00\u7f3a\u53e3\u5c06\u6025\u5267\u6269\u5927\u81f3400\u4e07,\u4e25\u91cd\u5236\u7ea6\u6211\u56fdAI\u4ea7\u4e1a\u7684\u521b\u65b0\u6b65\u4f10\u3002\u52a0\u5f3a\u4eba\u624d\u57f9\u517b,\u4f18\u5316\u6559\u80b2\u4f53\u7cfb,\u56fd\u9645\u5408\u4f5c\u5e76\u8fdb\u662f\u7834\u89e3\u56f0\u5c40\u3001\u63a8\u52a8AI\u53d1\u5c55\u7684\u5173\u952e\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121744-6adf44e83adb9.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261026121744-6adf44e854eb6.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h6>8\u3001\u9002\u5408\u8c01\u5b66&#xff1f;<\/h6>\n<ul>\n<li>\u4f20\u7edf\u540e\u7aef \/ Java \/ \u524d\u7aef\u5f00\u53d1&#xff0c;\u60f3\u8f6c\u578b AI \u5e94\u7528<\/li>\n<li>\u5927\u5b66\u751f\u3001\u5e94\u5c4a\u751f&#xff0c;\u60f3\u62ff\u66f4\u597d\u7684 offer<\/li>\n<li>\u4ea7\u54c1\u7ecf\u7406\u3001\u8fd0\u8425&#xff0c;\u60f3\u6b66\u88c5\u804c\u4e1a\u7ade\u4e89\u529b<\/li>\n<li>\u6280\u672f\u8d1f\u8d23\u4eba&#xff0c;\u60f3\u7ed9\u56e2\u961f\u843d\u5730\u63d0\u6548<\/li>\n<\/ul>\n<p>\u5b66\u4e60\u662f\u53cd\u4eba\u6027\u7684&#xff0c;\u4f46\u56de\u62a5\u662f\u771f\u91d1\u767d\u94f6\u3002\u6280\u672f\u4f1a\u66f4\u65b0&#xff0c;\u8d5b\u9053\u4f1a\u5207\u6362&#xff0c;\u4f46\u53ea\u8981\u4f60\u5148\u52a8\u624b&#xff0c;\u673a\u4f1a\u5c31\u6c38\u8fdc\u7ad9\u5728\u4f60\u8fd9\u8fb9\u3002<\/p>\n<h6>9\u3001\u8fd9\u4e9b\u8d44\u6599\u771f\u7684\u6709\u7528\u5417&#xff1f;<\/h6>\n<p>\u8fd9\u4efd\u8d44\u6599\u7531\u6211\u548c\u9c81\u4e3a\u6c11\u535a\u58eb(\u5317\u4eac\u6e05\u534e\u5927\u5b66\u5b66\u58eb\u548c\u7f8e\u56fd\u52a0\u5dde\u7406\u5de5\u5b66\u9662\u535a\u58eb)\u5171\u540c\u6574\u7406&#xff0c;\u73b0\u4efb\u4e0a\u6d77\u6bb7\u6cca\u4fe1\u606f\u79d1\u6280CEO&#xff0c;\u5176\u521b\u7acb\u7684MoPaaS\u4e91\u5e73\u53f0\u83b7Forrester\u5168\u7403\u2019\u5f3a\u52b2\u8868\u73b0\u8005\u2019\u8ba4\u8bc1&#xff0c;\u670d\u52a1\u822a\u5929\u79d1\u5de5\u3001\u56fd\u5bb6\u7535\u7f51\u7b491000&#043;\u4f01\u4e1a&#xff0c;\u4ee5\u7b2c\u4e00\u4f5c\u8005\u5728IEEE 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