{"id":56307,"date":"2025-08-14T16:35:31","date_gmt":"2025-08-14T08:35:31","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/56307.html"},"modified":"2025-08-14T16:35:31","modified_gmt":"2025-08-14T08:35:31","slug":"%e4%b8%80%e6%96%87%e5%90%83%e9%80%8f%ef%bc%817%e7%a7%8d%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%be%ae%e8%b0%83%e6%96%b9%e6%b3%95%e5%85%a8%e8%a7%a3%e6%9e%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/56307.html","title":{"rendered":"\u4e00\u6587\u5403\u900f\uff017\u79cd\u5927\u6a21\u578b\u5fae\u8c03\u65b9\u6cd5\u5168\u89e3\u6790"},"content":{"rendered":"

\u4e00\u6587\u5403\u900f！7\u79cd\u5927\u6a21\u578b\u5fae\u8c03\u65b9\u6cd5\u5168\u89e3\u6790<\/h2>\n

\u66f4\u591a\u5927\u6a21\u578b\u77e5\u8bc6\u5206\u4eab，\u5c3d\u5728>>>GitHub<<<<\/h3>\n

\u5927\u6a21\u578b\u5fae\u8c03：\u5f00\u542f\u5b9a\u5236\u5316 AI \u7684\u5927\u95e8<\/h3>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

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\u5168\u53c2\u6570\u5fae\u8c03（Full Fine-tuning）：\u5168\u9762\u5347\u7ea7<\/h3>\n

（\u4e00）\u5b9a\u4e49\u4e0e\u539f\u7406<\/h4>\n

\u5168\u53c2\u6570\u5fae\u8c03，\u6b63\u5982\u5176\u540d，\u662f\u4e00\u79cd\u5bf9\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u6240\u6709\u53c2\u6570\u8fdb\u884c\u66f4\u65b0\u548c\u8c03\u6574\u7684\u5fae\u8c03\u65b9\u5f0f\u3002\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d，\u6a21\u578b\u5c31\u50cf\u4e00\u4e2a\u6c42\u77e5\u6b32\u65fa\u76db\u7684\u5b66\u751f，\u5728\u5df2\u6709\u7684\u77e5\u8bc6\u50a8\u5907\u57fa\u7840\u4e0a，\u901a\u8fc7\u5b66\u4e60\u65b0\u7684\u4efb\u52a1\u6570\u636e，\u8fdb\u4e00\u6b65\u4f18\u5316\u81ea\u8eab\u7684 \u201c\u77e5\u8bc6\u4f53\u7cfb\u201d，\u4ee5\u66f4\u597d\u5730\u9002\u5e94\u7279\u5b9a\u4efb\u52a1\u7684\u9700\u6c42\u3002<\/p>\n

\u5176\u539f\u7406\u57fa\u4e8e\u68af\u5ea6\u4e0b\u964d\u7b49\u4f18\u5316\u7b97\u6cd5\u3002\u4ee5\u4e00\u4e2a\u7b80\u5355\u7684\u795e\u7ecf\u7f51\u7edc\u4e3a\u4f8b，\u5f53\u8f93\u5165\u7279\u5b9a\u4efb\u52a1\u7684\u6570\u636e\u65f6，\u6a21\u578b\u4f1a\u6839\u636e\u524d\u5411\u4f20\u64ad\u8ba1\u7b97\u51fa\u9884\u6d4b\u7ed3\u679c，\u7136\u540e\u901a\u8fc7\u4e0e\u771f\u5b9e\u6807\u7b7e\u7684\u5bf9\u6bd4，\u8ba1\u7b97\u51fa\u635f\u5931\u503c\u3002\u63a5\u7740，\u5229\u7528\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5，\u8ba1\u7b97\u51fa\u635f\u5931\u503c\u5173\u4e8e\u6bcf\u4e2a\u53c2\u6570\u7684\u68af\u5ea6，\u518d\u6839\u636e\u68af\u5ea6\u6765\u66f4\u65b0\u6240\u6709\u53c2\u6570，\u4f7f\u5f97\u6a21\u578b\u5728\u4e0b\u6b21\u9884\u6d4b\u65f6\u80fd\u591f\u66f4\u52a0\u51c6\u786e\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u5c31\u50cf\u662f\u5728\u4e0d\u65ad\u5730\u8c03\u6574\u6a21\u578b\u5185\u90e8\u7684 \u201c\u65cb\u94ae\u201d，\u4ee5\u8fbe\u5230\u6700\u4f73\u7684\u6027\u80fd\u72b6\u6001\u3002<\/p>\n

（\u4e8c）\u6d41\u7a0b\u6b65\u9aa4<\/h4>\n
  • \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b：\u4f7f\u7528\u5408\u9002\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6，\u5982 PyTorch \u6216 TensorFlow，\u52a0\u8f7d\u9884\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u3002\u4ee5 Hugging Face \u7684 Transformers \u5e93\u4e3a\u4f8b，\u52a0\u8f7d BERT \u6a21\u578b\u7684\u4ee3\u7801\u5982\u4e0b：<\/li>\n

    from transformers import AutoModelForSequenceClassification, AutoTokenizer<\/p>\n

    \\\\# \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u5206\u8bcd\u5668<\/p>\n

    model = AutoModelForSequenceClassification.from\\\\_pretrained('bert-base-uncased')<\/p>\n

    tokenizer = AutoTokenizer.from\\\\_pretrained('bert-base-uncased')<\/p>\n

  • \u51c6\u5907\u7279\u5b9a\u4efb\u52a1\u6570\u636e：\u6536\u96c6\u548c\u6574\u7406\u4e0e\u7279\u5b9a\u4efb\u52a1\u76f8\u5173\u7684\u6570\u636e，\u5e76\u8fdb\u884c\u5fc5\u8981\u7684\u9884\u5904\u7406\u3002\u6bd4\u5982\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d，\u9700\u8981\u5c06\u6587\u672c\u6570\u636e\u8fdb\u884c\u5206\u8bcd\u3001\u7f16\u7801，\u8f6c\u5316\u4e3a\u6a21\u578b\u80fd\u591f\u63a5\u53d7\u7684\u8f93\u5165\u683c\u5f0f，\u540c\u65f6\u6807\u6ce8\u597d\u6bcf\u4e2a\u6837\u672c\u7684\u7c7b\u522b\u6807\u7b7e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u9884\u5904\u7406\u793a\u4f8b：<\/li>\n

    import pandas as pd<\/p>\n

    from datasets import Dataset<\/p>\n

    \\\\# \u8bfb\u53d6\u6570\u636e<\/p>\n

    data = pd.read\\\\_csv('task\\\\_data.csv')<\/p>\n

    texts = data\\\\['text'].tolist()<\/p>\n

    labels = data\\\\['label'].tolist()<\/p>\n

    \\\\# \u6570\u636e\u7f16\u7801<\/p>\n

    encodings = tokenizer(texts, truncation=True, padding=True)<\/p>\n

    dataset = Dataset.from\\\\_dict({'input\\\\_ids': encodings\\\\['input\\\\_ids'], 'attention\\\\_mask': encodings\\\\['attention\\\\_mask'], 'labels': labels})<\/p>\n

  • \u4f7f\u7528\u4f18\u5316\u5668\u66f4\u65b0\u6240\u6709\u53c2\u6570：\u5b9a\u4e49\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570，\u7136\u540e\u901a\u8fc7\u8bad\u7ec3\u5faa\u73af\u6765\u66f4\u65b0\u6a21\u578b\u7684\u53c2\u6570\u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u6a21\u578b\u4f1a\u4e0d\u65ad\u5730\u6839\u636e\u5f53\u524d\u7684\u53c2\u6570\u8ba1\u7b97\u9884\u6d4b\u7ed3\u679c，\u8ba1\u7b97\u635f\u5931，\u5e76\u6839\u636e\u635f\u5931\u7684\u68af\u5ea6\u6765\u66f4\u65b0\u53c2\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u8bad\u7ec3\u5faa\u73af\u6846\u67b6：<\/li>\n

    import torch<\/p>\n

    from torch.utils.data import DataLoader<\/p>\n

    from transformers import AdamW<\/p>\n

    \\\\# \u5b9a\u4e49\u6570\u636e\u52a0\u8f7d\u5668<\/p>\n

    train\\\\_loader = DataLoader(dataset, batch\\\\_size=16, shuffle=True)<\/p>\n

    \\\\# \u5b9a\u4e49\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570<\/p>\n

    optimizer = AdamW(model.parameters(), lr=5e-5)<\/p>\n

    criterion = torch.nn.CrossEntropyLoss()<\/p>\n

    \\\\# \u8bad\u7ec3\u5faa\u73af<\/p>\n

    device = torch.device("cuda" if torch.cuda.is\\\\_available() else "cpu")<\/p>\n

    model.to(device)<\/p>\n

    model.train()<\/p>\n

    for epoch in range(3):<\/p>\n

    for batch in train\\\\_loader:<\/p>\n

    inputs = batch\\\\['input\\\\_ids'].to(device)<\/p>\n

    attention\\\\_mask = batch\\\\['attention\\\\_mask'].to(device)<\/p>\n

    labels = batch\\\\['labels'].to(device)<\/p>\n

    optimizer.zero\\\\_grad()<\/p>\n

    outputs = model(inputs, attention\\\\_mask=attention\\\\_mask, labels=labels)<\/p>\n

    loss = outputs.loss<\/p>\n

    loss.backward()<\/p>\n

    optimizer.step()<\/p>\n

    （\u4e09）\u9002\u7528\u573a\u666f<\/h4>\n

    \u5168\u53c2\u6570\u5fae\u8c03\u9002\u7528\u4e8e\u591a\u79cd\u573a\u666f，\u5c24\u5176\u662f\u90a3\u4e9b\u5bf9\u6a21\u578b\u6027\u80fd\u8981\u6c42\u6781\u9ad8，\u4e14\u62e5\u6709\u5145\u8db3\u8ba1\u7b97\u8d44\u6e90\u548c\u5927\u91cf\u6570\u636e\u7684\u4efb\u52a1\u3002\u5728\u533b\u7597\u9886\u57df，\u75be\u75c5\u8bca\u65ad\u6a21\u578b\u9700\u8981\u5bf9\u5404\u79cd\u590d\u6742\u7684\u75c7\u72b6\u548c\u75c5\u4f8b\u6570\u636e\u8fdb\u884c\u51c6\u786e\u5206\u6790，\u901a\u8fc7\u5168\u53c2\u6570\u5fae\u8c03，\u53ef\u4ee5\u8ba9\u6a21\u578b\u5145\u5206\u5b66\u4e60\u5927\u91cf\u7684\u533b\u5b66\u77e5\u8bc6\u548c\u75c5\u4f8b\u4fe1\u606f，\u4ece\u800c\u63d0\u9ad8\u8bca\u65ad\u7684\u51c6\u786e\u6027；\u5728\u91d1\u878d\u98ce\u9669\u8bc4\u4f30\u4e2d，\u5168\u53c2\u6570\u5fae\u8c03\u80fd\u591f\u8ba9\u6a21\u578b\u7efc\u5408\u8003\u8651\u5404\u79cd\u7ecf\u6d4e\u6307\u6807\u3001\u5e02\u573a\u8d8b\u52bf\u548c\u5386\u53f2\u6570\u636e，\u4e3a\u98ce\u9669\u8bc4\u4f30\u63d0\u4f9b\u66f4\u7cbe\u51c6\u7684\u7ed3\u679c\u3002<\/p>\n

    （\u56db）\u4f18\u7f3a\u70b9\u5206\u6790<\/h4>\n
  • \n

    \u4f18\u70b9：\u7531\u4e8e\u5bf9\u6240\u6709\u53c2\u6570\u8fdb\u884c\u4e86\u8c03\u6574，\u5168\u53c2\u6570\u5fae\u8c03\u80fd\u591f\u5145\u5206\u6316\u6398\u6a21\u578b\u7684\u6f5c\u529b，\u4f7f\u5f97\u6a21\u578b\u5728\u7279\u5b9a\u4efb\u52a1\u4e0a\u7684\u6027\u80fd\u5f97\u5230\u663e\u8457\u63d0\u5347，\u83b7\u5f97\u9ad8\u7cbe\u5ea6\u7684\u7ed3\u679c\u3002\u8fd9\u5c31\u597d\u6bd4\u4e00\u4f4d\u8fd0\u52a8\u5458\u901a\u8fc7\u5168\u9762\u7684\u8bad\u7ec3，\u5728\u6bd4\u8d5b\u4e2d\u53d1\u6325\u51fa\u81ea\u5df1\u7684\u6700\u4f73\u6c34\u5e73\u3002<\/p>\n<\/li>\n

  • \n

    \u7f3a\u70b9：\u5168\u53c2\u6570\u5fae\u8c03\u9700\u8981\u5bf9\u6240\u6709\u53c2\u6570\u8fdb\u884c\u8ba1\u7b97\u548c\u66f4\u65b0，\u8fd9\u65e0\u7591\u9700\u8981\u6d88\u8017\u5927\u91cf\u7684\u8ba1\u7b97\u8d44\u6e90，\u5982 GPU \u5185\u5b58\u548c\u8ba1\u7b97\u65f6\u95f4\u3002\u8bad\u7ec3\u4e00\u4e2a\u5927\u89c4\u6a21\u7684\u6a21\u578b\u53ef\u80fd\u9700\u8981\u6570\u5929\u751a\u81f3\u6570\u5468\u7684\u65f6\u95f4，\u6210\u672c\u8f83\u9ad8\u3002\u6b64\u5916，\u7531\u4e8e\u6a21\u578b\u5b66\u4e60\u7684\u53c2\u6570\u8f83\u591a，\u5982\u679c\u8bad\u7ec3\u6570\u636e\u4e0d\u8db3，\u5f88\u5bb9\u6613\u51fa\u73b0\u8fc7\u62df\u5408\u73b0\u8c61，\u5bfc\u81f4\u6a21\u578b\u5728\u65b0\u6570\u636e\u4e0a\u7684\u8868\u73b0\u4e0d\u4f73\u3002<\/p>\n<\/li>\n

    LoRA（Low-Rank Adaptation）：\u4f4e\u79e9\u77e9\u9635\u7684\u9b54\u6cd5<\/h3>\n

    （\u4e00）\u539f\u7406\u5256\u6790<\/h4>\n

    LoRA，\u5373\u4f4e\u79e9\u81ea\u9002\u5e94，\u662f\u4e00\u79cd\u65e8\u5728\u901a\u8fc7\u7528\u4f4e\u7ef4\u7ed3\u6784\u8fd1\u4f3c\u5927\u578b\u6a21\u578b\u7684\u9ad8\u7ef4\u7ed3\u6784\u6765\u964d\u4f4e\u5176\u590d\u6742\u6027\u7684\u6280\u672f ，\u4e5f\u662f\u5f53\u524d\u5927\u6a21\u578b\u5fae\u8c03\u9886\u57df\u4e2d\u5907\u53d7\u77a9\u76ee\u7684\u4e00\u79cd\u65b9\u6cd5\u3002\u5b83\u7684\u6838\u5fc3\u539f\u7406\u57fa\u4e8e\u4e00\u4e2a\u91cd\u8981\u5047\u8bbe：\u5728\u8bb8\u591a\u4efb\u52a1\u4e2d，\u5927\u578b\u6a21\u578b\u7684\u9ad8\u7ef4\u7ed3\u6784\u53ef\u80fd\u5305\u542b\u5197\u4f59\u6216\u4e0d\u76f8\u5173\u7684\u4fe1\u606f，\u800c\u771f\u6b63\u5bf9\u6a21\u578b\u6027\u80fd\u8d77\u5173\u952e\u4f5c\u7528\u7684\u6709\u6548\u4fe1\u606f\u5f80\u5f80\u5b58\u5728\u4e8e\u4e00\u4e2a\u8f83\u4f4e\u7ef4\u5ea6\u7684\u5b50\u7a7a\u95f4\u4e2d\u3002<\/p>\n

    \u4ee5 Transformer \u67b6\u6784\u7684\u5927\u6a21\u578b\u4e3a\u4f8b，\u5728\u4f20\u7edf\u7684\u5168\u53c2\u6570\u5fae\u8c03\u4e2d，\u6211\u4eec\u9700\u8981\u66f4\u65b0\u6a21\u578b\u4e2d\u7684\u6240\u6709\u53c2\u6570，\u8fd9\u4e0d\u4ec5\u8ba1\u7b97\u91cf\u5927，\u800c\u4e14\u5bb9\u6613\u5bfc\u81f4\u8fc7\u62df\u5408\u3002\u800c LoRA \u5219\u53e6\u8f9f\u8e4a\u5f84，\u5b83\u901a\u8fc7\u5728\u539f\u59cb\u6a21\u578b\u7684\u6743\u91cd\u77e9\u9635\u4e2d\u5f15\u5165\u4e24\u4e2a\u4f4e\u79e9\u77e9\u9635 A \u548c B，\u6765\u8fd1\u4f3c\u8868\u793a\u6a21\u578b\u5728\u7279\u5b9a\u4efb\u52a1\u4e0a\u7684\u53c2\u6570\u66f4\u65b0\u3002\u5177\u4f53\u6765\u8bf4，\u5047\u8bbe\u539f\u59cb\u6a21\u578b\u7684\u6743\u91cd\u77e9\u9635\u4e3a W，LoRA \u5f15\u5165\u7684\u4f4e\u79e9\u77e9\u9635 A \u548c B \u7684\u7ef4\u5ea6\u8fdc\u5c0f\u4e8e W \u7684\u7ef4\u5ea6，\u4f7f\u5f97\u65b0\u7684\u6743\u91cd\u77e9\u9635\u53ef\u4ee5\u8868\u793a\u4e3a<\/p>\n

    W<\/p>\n

    +<\/p>\n

    \u0394<\/p>\n

    W<\/p>\n

    W + \\\\Delta W<\/p>\n

    <\/span><\/span>W<\/span><\/span>+<\/span><\/span><\/span><\/span>\u0394<\/span>W<\/span><\/span><\/span><\/span><\/span>，\u5176\u4e2d<\/p>\n

    \u0394<\/p>\n

    W<\/p>\n

    =<\/p>\n

    A<\/p>\n

    \u00d7<\/p>\n

    B<\/p>\n

    \\\\Delta W = A \\\\times B<\/p>\n

    <\/span><\/span>\u0394<\/span>W<\/span><\/span>=<\/span><\/span><\/span><\/span>A<\/span><\/span>\u00d7<\/span><\/span><\/span><\/span>B<\/span><\/span><\/span><\/span><\/span> \u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，LoRA \u53ea\u5bf9\u4f4e\u79e9\u77e9\u9635 A \u548c B \u8fdb\u884c\u8bad\u7ec3，\u800c\u51bb\u7ed3\u539f\u59cb\u6a21\u578b\u7684\u6743\u91cd W，\u8fd9\u6837\u5927\u5927\u51cf\u5c11\u4e86\u9700\u8981\u8bad\u7ec3\u7684\u53c2\u6570\u6570\u91cf，\u4ece\u800c\u964d\u4f4e\u4e86\u8ba1\u7b97\u6210\u672c\u548c\u5185\u5b58\u9700\u6c42\u3002<\/p>\n

    （\u4e8c）\u64cd\u4f5c\u6d41\u7a0b<\/h4>\n
  • \n

    \u786e\u5b9a\u4f4e\u79e9\u77e9\u9635\u63d2\u5165\u4f4d\u7f6e：\u901a\u5e38\u5728 Transformer \u5c42\u7684\u5173\u952e\u90e8\u5206\u63d2\u5165\u4f4e\u79e9\u77e9\u9635，\u5982\u6ce8\u610f\u529b\u673a\u5236\u4e2d\u7684 query\u3001key\u3001value \u6295\u5f71\u77e9\u9635（<\/p>\n

    W<\/p>\n

    q<\/p>\n

    W_q<\/p>\n

    <\/span><\/span>W<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u3001<\/p>\n

    W<\/p>\n

    k<\/p>\n

    W_k<\/p>\n

    <\/span><\/span>W<\/span><\/span>k<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u3001<\/p>\n

    W<\/p>\n

    v<\/p>\n

    W_v<\/p>\n

    <\/span><\/span>W<\/span><\/span>v<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> ）\u4ee5\u53ca\u8f93\u51fa\u6295\u5f71\u77e9\u9635（<\/p>\n

    W<\/p>\n

    o<\/p>\n

    W_o<\/p>\n

    <\/span><\/span>W<\/span><\/span>o<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> ）\u3002\u4ee5\u6ce8\u610f\u529b\u673a\u5236\u4e2d\u7684<\/p>\n

    W<\/p>\n

    q<\/p>\n

    W_q<\/p>\n

    <\/span><\/span>W<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u77e9\u9635\u4e3a\u4f8b，\u5728\u539f\u59cb\u7684<\/p>\n

    W<\/p>\n

    q<\/p>\n

    W_q<\/p>\n

    <\/span><\/span>W<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u57fa\u7840\u4e0a，\u6dfb\u52a0\u4f4e\u79e9\u77e9\u9635\u7684\u7ed3\u6784，\u4f7f\u5f97<\/p>\n

    W<\/p>\n

    q<\/p>\n

    W_q<\/p>\n

    <\/span><\/span>W<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u53d8\u4e3a<\/p>\n

    W<\/p>\n

    q<\/p>\n

    +<\/p>\n

    \u0394<\/p>\n

    W<\/p>\n

    q<\/p>\n

    W_q + \\\\Delta W_q<\/p>\n

    <\/span><\/span>W<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>\u0394<\/span>W<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>，\u5176\u4e2d<\/p>\n

    \u0394<\/p>\n

    W<\/p>\n

    q<\/p>\n

    =<\/p>\n

    A<\/p>\n

    q<\/p>\n

    \u00d7<\/p>\n

    B<\/p>\n

    q<\/p>\n

    \\\\Delta W_q = A_q \\\\times B_q<\/p>\n

    <\/span><\/span>\u0394<\/span>W<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>A<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u00d7<\/span><\/span><\/span><\/span>B<\/span><\/span>q<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u3002<\/p>\n<\/li>\n

  • \n

    \u914d\u7f6e\u4f4e\u79e9\u77e9\u9635\u8d85\u53c2\u6570：\u9700\u8981\u8bbe\u7f6e\u4f4e\u79e9\u77e9\u9635\u7684\u79e9 r，\u5b83\u51b3\u5b9a\u4e86\u4f4e\u79e9\u77e9\u9635\u7684\u7ef4\u5ea6\u5927\u5c0f，r \u8d8a\u5c0f，\u53c2\u6570\u91cf\u51cf\u5c11\u5f97\u8d8a\u591a，\u4f46\u6a21\u578b\u7684\u8868\u8fbe\u80fd\u529b\u53ef\u80fd\u4f1a\u53d7\u5230\u4e00\u5b9a\u5f71\u54cd，\u4e00\u822c\u6839\u636e\u4efb\u52a1\u7684\u590d\u6742\u7a0b\u5ea6\u548c\u8d44\u6e90\u60c5\u51b5\u8fdb\u884c\u8c03\u6574，\u53d6\u503c\u8303\u56f4\u5728 4 – 64 \u4e4b\u95f4；\u7f29\u653e\u56e0\u5b50 alpha，\u7528\u4e8e\u8c03\u6574\u4f4e\u79e9\u77e9\u9635\u7684\u7f29\u653e\u6bd4\u4f8b，\u901a\u5e38\u8bbe\u4e3a\u79e9 r \u7684 2 \u500d，\u4ee5\u786e\u4fdd\u8bad\u7ec3\u7684\u7a33\u5b9a\u6027；\u8fd8\u53ef\u4ee5\u8bbe\u7f6e Dropout \u6982\u7387，\u9632\u6b62\u8fc7\u62df\u5408，\u4e00\u822c\u8bbe\u4e3a 0.1 – 0.3 \u3002<\/p>\n<\/li>\n

  • \n

    \u53ea\u8bad\u7ec3\u4f4e\u79e9\u77e9\u9635：\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u4f7f\u7528\u4f18\u5316\u5668（\u5982 AdamW）\u4ec5\u5bf9\u4f4e\u79e9\u77e9\u9635 A \u548c B \u7684\u53c2\u6570\u8fdb\u884c\u66f4\u65b0，\u800c\u4fdd\u6301\u539f\u59cb\u6a21\u578b\u6743\u91cd\u4e0d\u53d8\u3002\u4ee5 PyTorch \u4ee3\u7801\u4e3a\u4f8b：<\/p>\n<\/li>\n

    import torch<\/p>\n

    import torch.nn as nn<\/p>\n

    from transformers import AutoModel, AdamW<\/p>\n

    \\\\# \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/p>\n

    base\\\\_model = AutoModel.from\\\\_pretrained('bert-base-uncased')<\/p>\n

    \\\\# \u5b9a\u4e49LoRA\u5c42<\/p>\n

    class LoRALayer(nn.Module):<\/p>\n

    def \\\\_\\\\_init\\\\_\\\\_(self, in\\\\_features, out\\\\_features, rank=4):<\/p>\n

    super().\\\\_\\\\_init\\\\_\\\\_()<\/p>\n

    self.A = nn.Parameter(torch.randn(rank, in\\\\_features))<\/p>\n

    self.B = nn.Parameter(torch.randn(out\\\\_features, rank))<\/p>\n

    def forward(self, x):<\/p>\n

    return self.B @ self.A @ x<\/p>\n

    \\\\# \u5728\u6a21\u578b\u4e2d\u63d2\u5165LoRA\u5c42，\u8fd9\u91cc\u5047\u8bbe\u5728\u67d0\u4e00\u5c42\u7684\u7ebf\u6027\u53d8\u6362\u4e2d\u63d2\u5165<\/p>\n

    class LoRAModel(nn.Module):<\/p>\n

    def \\\\_\\\\_init\\\\_\\\\_(self, base\\\\_model):<\/p>\n

    super().\\\\_\\\\_init\\\\_\\\\_()<\/p>\n

    self.base\\\\_model = base\\\\_model<\/p>\n

    self.lora\\\\_layer = LoRALayer(base\\\\_model.config.hidden\\\\_size, base\\\\_model.config.hidden\\\\_size)<\/p>\n

    def forward(self, input\\\\_ids, attention\\\\_mask):<\/p>\n

    outputs = self.base\\\\_model(input\\\\_ids, attention\\\\_mask=attention\\\\_mask)<\/p>\n

    hidden\\\\_states = outputs.last\\\\_hidden\\\\_state<\/p>\n

    lora\\\\_output = self.lora\\\\_layer(hidden\\\\_states)<\/p>\n

    return hidden\\\\_states + lora\\\\_output<\/p>\n

    lora\\\\_model = LoRAModel(base\\\\_model)<\/p>\n

    \\\\# \u51bb\u7ed3\u539f\u59cb\u6a21\u578b\u53c2\u6570<\/p>\n

    for param in lora\\\\_model.base\\\\_model.parameters():<\/p>\n

    param.requires\\\\_grad = False<\/p>\n

    \\\\# \u5b9a\u4e49\u4f18\u5316\u5668，\u53ea\u5bf9LoRA\u5c42\u53c2\u6570\u8fdb\u884c\u4f18\u5316<\/p>\n

    optimizer = AdamW(lora\\\\_model.lora\\\\_layer.parameters(), lr=1e-4)<\/p>\n

    （\u4e09）\u5e94\u7528\u573a\u666f<\/h4>\n

    LoRA \u9002\u7528\u4e8e\u591a\u79cd\u573a\u666f，\u5c24\u5176\u662f\u5728\u8d44\u6e90\u53d7\u9650\u7684\u73af\u5883\u4e0b，\u5982\u4f7f\u7528\u6d88\u8d39\u7ea7\u663e\u5361\u8fdb\u884c\u6a21\u578b\u5fae\u8c03\u65f6，LoRA \u80fd\u591f\u5728\u6709\u9650\u7684\u8ba1\u7b97\u8d44\u6e90\u4e0b\u5b8c\u6210\u4efb\u52a1；\u5728\u591a\u4efb\u52a1\u5fae\u8c03\u573a\u666f\u4e2d，LoRA \u53ef\u4ee5\u5feb\u901f\u5730\u5c06\u9884\u8bad\u7ec3\u6a21\u578b\u9002\u914d\u5230\u591a\u4e2a\u4e0d\u540c\u7684\u4efb\u52a1\u4e0a，\u6bcf\u4e2a\u4efb\u52a1\u53ea\u9700\u8bad\u7ec3\u5c11\u91cf\u7684\u4f4e\u79e9\u77e9\u9635\u53c2\u6570，\u5927\u5927\u63d0\u9ad8\u4e86\u6548\u7387\u3002\u5728\u533b\u7597\u9886\u57df，\u533b\u751f\u53ef\u80fd\u9700\u8981\u5728\u672c\u5730\u8bbe\u5907\u4e0a\u5bf9\u533b\u5b66\u56fe\u50cf\u5206\u6790\u6a21\u578b\u8fdb\u884c\u5fae\u8c03，\u4ee5\u9002\u5e94\u7279\u5b9a\u533b\u9662\u7684\u75c5\u4f8b\u6570\u636e，LoRA \u7684\u4f4e\u8d44\u6e90\u9700\u6c42\u4f7f\u5176\u6210\u4e3a\u7406\u60f3\u9009\u62e9；\u5728\u667a\u80fd\u5ba2\u670d\u7cfb\u7edf\u4e2d，\u9700\u8981\u5c06\u8bed\u8a00\u6a21\u578b\u5feb\u901f\u9002\u914d\u5230\u4e0d\u540c\u4e1a\u52a1\u9886\u57df\u7684\u95ee\u7b54\u4efb\u52a1，LoRA \u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002<\/p>\n

    （\u56db）\u4f18\u52bf\u4e0e\u5c40\u9650<\/h4>\n
  • \n

    \u4f18\u52bf：LoRA \u663e\u8457\u51cf\u5c11\u4e86\u8ba1\u7b97\u91cf\u548c\u5185\u5b58\u9700\u6c42，\u76f8\u6bd4\u5168\u53c2\u6570\u5fae\u8c03，\u5176\u8bad\u7ec3\u901f\u5ea6\u66f4\u5feb，\u6210\u672c\u66f4\u4f4e，\u8fd9\u4f7f\u5f97\u5728\u8d44\u6e90\u6709\u9650\u7684\u60c5\u51b5\u4e0b\u4e5f\u80fd\u5bf9\u5927\u6a21\u578b\u8fdb\u884c\u6709\u6548\u5fae\u8c03；\u7531\u4e8e\u53ea\u9700\u8981\u8bad\u7ec3\u5c11\u91cf\u7684\u4f4e\u79e9\u77e9\u9635\u53c2\u6570，LoRA \u53ef\u4ee5\u5feb\u901f\u5730\u5c06\u6a21\u578b\u9002\u914d\u5230\u591a\u4e2a\u4e0d\u540c\u7684\u4efb\u52a1\u4e0a，\u63d0\u9ad8\u4e86\u6a21\u578b\u7684\u7075\u6d3b\u6027\u548c\u901a\u7528\u6027\u3002<\/p>\n<\/li>\n

  • \n

    \u5c40\u9650：\u4f7f\u7528\u4f4e\u79e9\u77e9\u9635\u8fd1\u4f3c\u539f\u59cb\u6a21\u578b\u7684\u9ad8\u7ef4\u7ed3\u6784\u53ef\u80fd\u4f1a\u5f15\u5165\u4e00\u4e9b\u8fd1\u4f3c\u8bef\u5dee，\u4ece\u800c\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u5f71\u54cd\u6a21\u578b\u5728\u67d0\u4e9b\u590d\u6742\u4efb\u52a1\u4e0a\u7684\u6027\u80fd，\u5c24\u5176\u662f\u90a3\u4e9b\u9700\u8981\u7ec6\u7c92\u5ea6\u7406\u89e3\u6216\u4e0e\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u8bad\u7ec3\u5206\u5e03\u6709\u5f88\u5927\u4e0d\u540c\u7684\u4efb\u52a1；\u5728\u67d0\u4e9b\u6781\u7aef\u60c5\u51b5\u4e0b，LoRA \u7684\u8868\u73b0\u53ef\u80fd\u4e0d\u5982\u5168\u53c2\u6570\u5fae\u8c03，\u56e0\u4e3a\u5b83\u53ea\u80fd\u5b66\u4e60\u5230\u4f4e\u79e9\u5b50\u7a7a\u95f4\u4e2d\u7684\u4fe1\u606f，\u5bf9\u4e8e\u4e00\u4e9b\u9ad8\u7ef4\u590d\u6742\u7279\u5f81\u7684\u6355\u6349\u80fd\u529b\u76f8\u5bf9\u8f83\u5f31 \u3002<\/p>\n<\/li>\n

    Prompt Tuning：\u63d0\u793a\u7684\u827a\u672f<\/h3>\n

    （\u4e00）\u57fa\u672c\u6982\u5ff5<\/h4>\n

    Prompt Tuning，\u5373\u63d0\u793a\u8c03\u6574，\u662f\u4e00\u79cd\u72ec\u7279\u7684\u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f，\u5b83\u4e13\u6ce8\u4e8e\u901a\u8fc7\u4f18\u5316\u8f93\u5165\u63d0\u793a（Prompt）\u6765\u5de7\u5999\u5730\u8c03\u6574\u6a21\u578b\u7684\u884c\u4e3a，\u800c\u65e0\u9700\u5bf9\u6a21\u578b\u7684\u6838\u5fc3\u53c2\u6570\u8fdb\u884c\u4efb\u4f55\u6539\u53d8\u3002\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d，\u5c31\u50cf\u662f\u7ed9\u6a21\u578b\u4e00\u4e2a\u7cbe\u5fc3\u8bbe\u8ba1\u7684 \u201c\u5f15\u5bfc\u8bed\u201d，\u8ba9\u5b83\u5728\u5904\u7406\u4efb\u52a1\u65f6\u80fd\u591f\u671d\u7740\u6211\u4eec\u671f\u671b\u7684\u65b9\u5411\u8fdb\u884c\u601d\u8003\u548c\u8f93\u51fa\u3002<\/p>\n

    （\u4e8c）\u5de5\u4f5c\u65b9\u5f0f<\/h4>\n
  • \n

    \u8f6f\u63d0\u793a（Soft Prompt）：\u8f6f\u63d0\u793a\u662f\u4e00\u79cd\u53ef\u8bad\u7ec3\u7684\u8fde\u7eed\u5411\u91cf，\u5b83\u5c31\u50cf\u662f\u6a21\u578b\u7684 \u201c\u79c1\u4eba\u52a9\u7406\u201d，\u80fd\u591f\u6839\u636e\u4efb\u52a1\u7684\u9700\u6c42\u8fdb\u884c\u81ea\u6211\u8c03\u6574\u548c\u4f18\u5316\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d，\u8f6f\u63d0\u793a\u901a\u5e38\u88ab\u6dfb\u52a0\u5230\u8f93\u5165\u6587\u672c\u7684\u524d\u9762，\u4e0e\u539f\u59cb\u8f93\u5165\u4e00\u8d77\u4f20\u9012\u7ed9\u6a21\u578b\u3002\u4ee5\u60c5\u611f\u5206\u6790\u4efb\u52a1\u4e3a\u4f8b，\u6a21\u578b\u7684\u8f93\u5165\u53ef\u80fd\u662f：\u5b66\u4e60\u5230\u7684\u8f6f\u63d0\u793a：[soft_prompt] \u8f93\u5165\u6587\u672c：\u8fd9\u662f\u4e00\u90e8\u975e\u5e38\u611f\u4eba\u7684\u7535\u5f71 \u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u8f6f\u63d0\u793a\u5411\u91cf\u4f1a\u901a\u8fc7\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5\u4e0d\u65ad\u5730\u88ab\u66f4\u65b0，\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u4efb\u52a1\u9700\u6c42，\u4ece\u800c\u5f15\u5bfc\u6a21\u578b\u751f\u6210\u66f4\u51c6\u786e\u7684\u60c5\u611f\u5206\u6790\u7ed3\u679c\u3002<\/p>\n<\/li>\n

  • \n

    \u786c\u63d0\u793a（Hard Prompt）：\u786c\u63d0\u793a\u5219\u662f\u4f7f\u7528\u56fa\u5b9a\u7684\u6587\u672c\u4f5c\u4e3a\u63d0\u793a，\u5b83\u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u9884\u5148\u8bbe\u5b9a\u597d\u7684 \u201c\u6a21\u677f\u201d，\u76f4\u63a5\u4e3a\u6a21\u578b\u63d0\u4f9b\u660e\u786e\u7684\u4efb\u52a1\u6307\u5bfc\u3002\u6bd4\u5982\u5728\u8fdb\u884c\u6587\u672c\u5206\u7c7b\u65f6，\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u786c\u63d0\u793a \u201c\u8bf7\u5224\u65ad\u4e0b\u9762\u7684\u6587\u672c\u5c5e\u4e8e\u54ea\u4e00\u7c7b：\u201d，\u7136\u540e\u5c06\u5f85\u5206\u7c7b\u7684\u6587\u672c\u63a5\u5728\u5176\u540e\u4f5c\u4e3a\u6a21\u578b\u7684\u8f93\u5165\u3002\u786c\u63d0\u793a\u7684\u4f18\u70b9\u662f\u7b80\u5355\u76f4\u63a5，\u5bb9\u6613\u5b9e\u73b0，\u9002\u5408\u4e8e\u4e00\u4e9b\u7b80\u5355\u7684\u4efb\u52a1\u3002\u7136\u800c，\u5b83\u7684\u7075\u6d3b\u6027\u8f83\u5dee，\u96be\u4ee5\u9488\u5bf9\u4e0d\u540c\u7684\u4efb\u52a1\u8bbe\u8ba1\u51fa\u6700\u4f18\u7684\u63d0\u793a，\u5e76\u4e14\u975e\u5e38\u4f9d\u8d56\u4eba\u5de5\u8bbe\u8ba1\u3002<\/p>\n<\/li>\n

    （\u4e09）\u5e94\u7528\u573a\u666f<\/h4>\n

    Prompt Tuning \u5728\u96f6\u6837\u672c\u6216\u5c11\u6837\u672c\u5b66\u4e60\u573a\u666f\u4e2d\u8868\u73b0\u51fa\u8272\u3002\u5728\u8fd9\u4e9b\u573a\u666f\u4e0b，\u6570\u636e\u91cf\u6781\u4e3a\u6709\u9650，\u4f20\u7edf\u7684\u5fae\u8c03\u65b9\u6cd5\u5f80\u5f80\u96be\u4ee5\u53d1\u6325\u4f5c\u7528\u3002\u800c Prompt Tuning \u901a\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u63d0\u793a，\u80fd\u591f\u5145\u5206\u6316\u6398\u6a21\u578b\u5728\u9884\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5b66\u4e60\u5230\u7684\u77e5\u8bc6，\u4f7f\u5f97\u6a21\u578b\u5728\u51e0\u4e4e\u6ca1\u6709\u6837\u672c\u7684\u60c5\u51b5\u4e0b\u4e5f\u80fd\u5b8c\u6210\u4efb\u52a1\u3002\u4f8b\u5982，\u5728\u533b\u7597\u9886\u57df，\u5f53\u9762\u5bf9\u7f55\u89c1\u75c5\u7684\u8bca\u65ad\u65f6，\u53ef\u80fd\u53ea\u6709\u6781\u5c11\u6570\u7684\u75c5\u4f8b\u6570\u636e\u53ef\u4f9b\u53c2\u8003，\u6b64\u65f6 Prompt Tuning \u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u5229\u7528\u5df2\u6709\u7684\u533b\u5b66\u77e5\u8bc6\u548c\u5c11\u91cf\u75c5\u4f8b\u4fe1\u606f，\u505a\u51fa\u521d\u6b65\u7684\u8bca\u65ad\u5efa\u8bae\u3002\u5728\u5feb\u901f\u9a8c\u8bc1\u65b0\u60f3\u6cd5\u65b9\u9762，Prompt Tuning \u4e5f\u5177\u6709\u72ec\u7279\u7684\u4f18\u52bf\u3002\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u63d0\u793a，\u5feb\u901f\u5730\u8ba9\u6a21\u578b\u5c1d\u8bd5\u4e0d\u540c\u7684\u4efb\u52a1\u6216\u65b9\u6cd5，\u4ece\u800c\u8282\u7701\u5927\u91cf\u7684\u65f6\u95f4\u548c\u8ba1\u7b97\u8d44\u6e90\u3002<\/p>\n

    （\u56db）\u7279\u70b9\u5206\u6790<\/h4>\n
  • \n

    \u4f18\u70b9：Prompt Tuning \u7684\u8bad\u7ec3\u6210\u672c\u6781\u4f4e，\u56e0\u4e3a\u5b83\u53ea\u9700\u8981\u8c03\u6574\u63d0\u793a\u5411\u91cf，\u800c\u65e0\u9700\u5bf9\u6a21\u578b\u7684\u5927\u91cf\u53c2\u6570\u8fdb\u884c\u66f4\u65b0，\u8fd9\u4f7f\u5f97\u5728\u8d44\u6e90\u6709\u9650\u7684\u60c5\u51b5\u4e0b\u4e5f\u80fd\u8f7b\u677e\u5b9e\u73b0\u5fae\u8c03；\u90e8\u7f72\u4e5f\u975e\u5e38\u7b80\u5355，\u53ea\u9700\u8981\u5c06\u8bad\u7ec3\u597d\u7684\u63d0\u793a\u5411\u91cf\u4e0e\u6a21\u578b\u7ed3\u5408\u5373\u53ef；Prompt Tuning \u8fd8\u80fd\u8ba9\u6a21\u578b\u5feb\u901f\u9002\u5e94\u65b0\u4efb\u52a1，\u63d0\u9ad8\u4e86\u6a21\u578b\u7684\u7075\u6d3b\u6027\u548c\u901a\u7528\u6027\u3002<\/p>\n<\/li>\n

  • \n

    \u7f3a\u70b9：\u7531\u4e8e\u4e0d\u6539\u53d8\u6a21\u578b\u7684\u6838\u5fc3\u53c2\u6570，Prompt Tuning \u5bf9\u7279\u5b9a\u4efb\u52a1\u7684\u9002\u914d\u80fd\u529b\u76f8\u5bf9\u6709\u9650，\u5728\u5904\u7406\u4e00\u4e9b\u590d\u6742\u4efb\u52a1\u65f6，\u53ef\u80fd\u65e0\u6cd5\u8fbe\u5230\u4e0e\u5168\u53c2\u6570\u5fae\u8c03\u76f8\u540c\u7684\u6027\u80fd；\u5176\u6548\u679c\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u4f9d\u8d56\u4e8e\u9ad8\u8d28\u91cf\u7684\u63d0\u793a\u8bbe\u8ba1，\u5982\u679c\u63d0\u793a\u8bbe\u8ba1\u4e0d\u5408\u7406，\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u7684\u8f93\u51fa\u7ed3\u679c\u4e0d\u7406\u60f3\u3002<\/p>\n<\/li>\n

    Prefix Tuning：\u524d\u7f00\u5f15\u5bfc<\/h3>\n

    （\u4e00）\u539f\u7406\u4ecb\u7ecd<\/h4>\n

    Prefix Tuning \u662f\u4e00\u79cd\u6781\u5177\u521b\u65b0\u6027\u7684\u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f，\u5b83\u7684\u6838\u5fc3\u5728\u4e8e\u901a\u8fc7\u5728\u6a21\u578b\u8f93\u5165\u524d\u6dfb\u52a0\u4e00\u7cfb\u5217\u53ef\u8bad\u7ec3\u7684\u524d\u7f00\u5411\u91cf，\u6765\u5de7\u5999\u5730\u5f15\u5bfc\u6a21\u578b\u751f\u6210\u7b26\u5408\u4efb\u52a1\u9700\u6c42\u7684\u8f93\u51fa\u3002\u8fd9\u4e9b\u524d\u7f00\u5411\u91cf\u5c31\u50cf\u662f\u7ed9\u6a21\u578b\u7684\u4e00\u4efd \u201c\u4efb\u52a1\u6307\u5357\u201d，\u5728\u6a21\u578b\u5904\u7406\u8f93\u5165\u6570\u636e\u65f6，\u5b83\u4eec\u4f1a\u4e0e\u8f93\u5165\u5e8f\u5217\u4e00\u540c\u53c2\u4e0e\u6a21\u578b\u7684\u8ba1\u7b97\u8fc7\u7a0b，\u4e3a\u6a21\u578b\u63d0\u4f9b\u989d\u5916\u7684\u4e0a\u4e0b\u6587\u4fe1\u606f，\u4ece\u800c\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u5728\u4e0d\u6539\u53d8\u539f\u6709\u53c2\u6570\u7684\u60c5\u51b5\u4e0b，\u66f4\u597d\u5730\u9002\u5e94\u7279\u5b9a\u7684\u4e0b\u6e38\u4efb\u52a1\u3002<\/p>\n

    （\u4e8c）\u5b9e\u73b0\u6b65\u9aa4<\/h4>\n
  • \u751f\u6210\u524d\u7f00\u5411\u91cf：\u9996\u5148，\u9700\u8981\u521d\u59cb\u5316\u4e00\u4e2a\u524d\u7f00\u5411\u91cf\u77e9\u9635，\u5176\u5f62\u72b6\u901a\u5e38\u4e3a [batch_size, prefix_length, hidden_size] ，\u5176\u4e2d batch_size \u8868\u793a\u4e00\u6b21\u5904\u7406\u7684\u6570\u636e\u6837\u672c\u6570\u91cf，prefix_length \u662f\u524d\u7f00\u7684\u957f\u5ea6，hidden_size \u662f\u6a21\u578b\u9690\u85cf\u5c42\u7684\u7ef4\u5ea6\u3002\u524d\u7f00\u5411\u91cf\u7684\u521d\u59cb\u5316\u53ef\u4ee5\u91c7\u7528\u968f\u673a\u521d\u59cb\u5316\u7684\u65b9\u5f0f，\u4e5f\u53ef\u4ee5\u57fa\u4e8e\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u67d0\u4e9b\u7279\u5f81\u8fdb\u884c\u521d\u59cb\u5316，\u4ee5\u63d0\u9ad8\u8bad\u7ec3\u7684\u7a33\u5b9a\u6027\u548c\u6548\u679c\u3002\u4f8b\u5982，\u4f7f\u7528 PyTorch \u751f\u6210\u4e00\u4e2a\u968f\u673a\u521d\u59cb\u5316\u7684\u524d\u7f00\u5411\u91cf：<\/li>\n

    import torch<\/p>\n

    \\\\# \u5047\u8bbebatch\\\\_size\u4e3a16，prefix\\\\_length\u4e3a10，hidden\\\\_size\u4e3a768<\/p>\n

    prefix\\\\_length = 10<\/p>\n

    hidden\\\\_size = 768<\/p>\n

    batch\\\\_size = 16<\/p>\n

    prefix\\\\_embeddings = torch.randn(batch\\\\_size, prefix\\\\_length, hidden\\\\_size)<\/p>\n

  • \u62fc\u63a5\u524d\u7f00\u4e0e\u8f93\u5165\u6570\u636e：\u5c06\u751f\u6210\u7684\u524d\u7f00\u5411\u91cf\u4e0e\u539f\u59cb\u8f93\u5165\u6570\u636e\u8fdb\u884c\u62fc\u63a5，\u7136\u540e\u5c06\u62fc\u63a5\u540e\u7684\u7ed3\u679c\u8f93\u5165\u5230\u6a21\u578b\u4e2d\u3002\u5728 Transformer \u6a21\u578b\u4e2d，\u901a\u5e38\u662f\u5c06\u524d\u7f00\u5411\u91cf\u6ce8\u5165\u5230\u6ce8\u610f\u529b\u5c42\u7684\u952e（Key）\u548c\u503c（Value）\u77e9\u9635\u4e2d，\u4e0e\u539f\u59cb\u8f93\u5165\u4e00\u8d77\u53c2\u4e0e\u6ce8\u610f\u529b\u8ba1\u7b97\u3002\u4ee5 Hugging Face \u7684 Transformer \u5e93\u4e3a\u4f8b，\u5b9e\u73b0\u5c06\u524d\u7f00\u5411\u91cf\u4e0e\u8f93\u5165\u6570\u636e\u62fc\u63a5\u5e76\u8f93\u5165\u6a21\u578b\u7684\u4ee3\u7801\u5982\u4e0b：<\/li>\n

    from transformers import AutoModel, AutoTokenizer<\/p>\n

    \\\\# \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u5206\u8bcd\u5668<\/p>\n

    model = AutoModel.from\\\\_pretrained('bert-base-uncased')<\/p>\n

    tokenizer = AutoTokenizer.from\\\\_pretrained('bert-base-uncased')<\/p>\n

    \\\\# \u539f\u59cb\u8f93\u5165\u6587\u672c<\/p>\n

    input\\\\_text = "\u8fd9\u662f\u4e00\u4e2a\u6d4b\u8bd5\u53e5\u5b50\u3002"<\/p>\n

    inputs = tokenizer(input\\\\_text, return\\\\_tensors='pt')<\/p>\n

    \\\\# \u5c06\u524d\u7f00\u5411\u91cf\u4e0e\u8f93\u5165\u6570\u636e\u62fc\u63a5<\/p>\n

    input\\\\_ids = torch.cat(\\\\[prefix\\\\_embeddings, inputs\\\\['input\\\\_ids']], dim=1)<\/p>\n

    attention\\\\_mask = torch.cat(\\\\[torch.ones(batch\\\\_size, prefix\\\\_length), inputs\\\\['attention\\\\_mask']], dim=1)<\/p>\n

    \\\\# \u5c06\u62fc\u63a5\u540e\u7684\u7ed3\u679c\u8f93\u5165\u6a21\u578b<\/p>\n

    outputs = model(input\\\\_ids, attention\\\\_mask=attention\\\\_mask)<\/p>\n

    （\u4e09）\u9002\u7528\u8303\u56f4<\/h4>\n

    Prefix Tuning \u5728\u591a\u79cd\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u90fd\u6709\u51fa\u8272\u7684\u8868\u73b0，\u7279\u522b\u9002\u5408\u90a3\u4e9b\u9700\u8981\u5bf9\u6a21\u578b\u8f93\u51fa\u8fdb\u884c\u7279\u5b9a\u63a7\u5236\u7684\u4efb\u52a1\u3002\u5728\u6587\u672c\u6458\u8981\u4efb\u52a1\u4e2d，\u901a\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1\u524d\u7f00\u5411\u91cf，\u53ef\u4ee5\u5f15\u5bfc\u6a21\u578b\u751f\u6210\u66f4\u7b80\u6d01\u3001\u51c6\u786e\u7684\u6458\u8981；\u5728\u7279\u5b9a\u98ce\u683c\u6587\u672c\u751f\u6210\u4efb\u52a1\u4e2d，\u5982\u751f\u6210\u8bd7\u6b4c\u3001\u5c0f\u8bf4\u7b49，Prefix Tuning \u80fd\u591f\u8ba9\u6a21\u578b\u6839\u636e\u524d\u7f00\u7684\u5f15\u5bfc，\u751f\u6210\u7b26\u5408\u7279\u5b9a\u98ce\u683c\u548c\u4e3b\u9898\u7684\u6587\u672c\u3002\u6b64\u5916，\u7531\u4e8e Prefix Tuning \u53ea\u9700\u8981\u66f4\u65b0\u524d\u7f00\u5411\u91cf\u7684\u53c2\u6570，\u800c\u4e0d\u9700\u8981\u66f4\u65b0\u6574\u4e2a\u6a21\u578b\u7684\u53c2\u6570，\u56e0\u6b64\u5728\u5b9e\u65f6\u5bf9\u8bdd\u7cfb\u7edf\u6216\u8fb9\u7f18\u8bbe\u5907\u90e8\u7f72\u7b49\u573a\u666f\u4e2d，\u4e5f\u5177\u6709\u660e\u663e\u7684\u4f18\u52bf，\u80fd\u591f\u5feb\u901f\u9002\u5e94\u4e0d\u540c\u7684\u4efb\u52a1\u9700\u6c42，\u540c\u65f6\u51cf\u5c11\u8ba1\u7b97\u8d44\u6e90\u7684\u6d88\u8017\u3002<\/p>\n

    （\u56db）\u4f18\u7f3a\u70b9<\/h4>\n
  • \n

    \u4f18\u70b9：Prefix Tuning \u7684\u6700\u5927\u4f18\u70b9\u4e4b\u4e00\u662f\u80fd\u591f\u6709\u6548\u5730\u63a7\u5236\u6a21\u578b\u7684\u8f93\u51fa\u65b9\u5411，\u901a\u8fc7\u8c03\u6574\u524d\u7f00\u5411\u91cf，\u6211\u4eec\u53ef\u4ee5\u7075\u6d3b\u5730\u5f15\u5bfc\u6a21\u578b\u751f\u6210\u6211\u4eec\u671f\u671b\u7684\u7ed3\u679c\u3002\u800c\u4e14，\u7531\u4e8e\u5b83\u4e0d\u9700\u8981\u6539\u53d8\u6a21\u578b\u7684\u4e3b\u4f53\u7ed3\u6784，\u53ea\u9700\u8981\u8bad\u7ec3\u5c11\u91cf\u7684\u524d\u7f00\u5411\u91cf\u53c2\u6570，\u5927\u5927\u51cf\u5c11\u4e86\u8ba1\u7b97\u8d44\u6e90\u7684\u6d88\u8017\u548c\u8bad\u7ec3\u65f6\u95f4，\u540c\u65f6\u4e5f\u964d\u4f4e\u4e86\u6a21\u578b\u8fc7\u62df\u5408\u7684\u98ce\u9669\u3002\u6b64\u5916，Prefix Tuning \u8fd8\u5177\u6709\u826f\u597d\u7684\u53ef\u6269\u5c55\u6027，\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u5e94\u7528\u4e8e\u4e0d\u540c\u7684\u6a21\u578b\u67b6\u6784\u548c\u4efb\u52a1\u4e2d\u3002<\/p>\n<\/li>\n

  • \n

    \u7f3a\u70b9：Prefix Tuning \u7684\u6548\u679c\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u4f9d\u8d56\u4e8e\u524d\u7f00\u7684\u8bbe\u8ba1\u548c\u8c03\u6574\u3002\u5982\u679c\u524d\u7f00\u957f\u5ea6\u8bbe\u7f6e\u4e0d\u5f53，\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u65e0\u6cd5\u5145\u5206\u5b66\u4e60\u5230\u4efb\u52a1\u76f8\u5173\u7684\u4fe1\u606f，\u4ece\u800c\u5f71\u54cd\u6027\u80fd；\u524d\u7f00\u5411\u91cf\u7684\u521d\u59cb\u5316\u548c\u8bad\u7ec3\u4e5f\u9700\u8981\u4e00\u5b9a\u7684\u6280\u5de7\u548c\u7ecf\u9a8c，\u4e0d\u5408\u9002\u7684\u521d\u59cb\u5316\u6216\u8bad\u7ec3\u65b9\u6cd5\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u6536\u655b\u56f0\u96be\u3002\u6b64\u5916，\u6dfb\u52a0\u524d\u7f00\u5411\u91cf\u4f1a\u589e\u52a0\u6a21\u578b\u8f93\u5165\u7684\u957f\u5ea6，\u8fd9\u5728\u4e00\u4e9b\u5bf9\u8f93\u5165\u957f\u5ea6\u6709\u9650\u5236\u7684\u573a\u666f\u4e2d\u53ef\u80fd\u4f1a\u6210\u4e3a\u4e00\u4e2a\u95ee\u9898，\u9700\u8981\u8fdb\u884c\u989d\u5916\u7684\u5904\u7406\u3002<\/p>\n<\/li>\n

    Adapter Tuning：\u9002\u914d\u5668\u52a0\u6301<\/h3>\n

    （\u4e00）\u9002\u914d\u5668\u539f\u7406<\/h4>\n

    Adapter Tuning \u662f\u4e00\u79cd\u6781\u5177\u521b\u65b0\u6027\u7684\u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f，\u5b83\u7684\u6838\u5fc3\u5728\u4e8e\u5728\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u5c42\u4e0e\u5c42\u4e4b\u95f4\u5de7\u5999\u5730\u63d2\u5165\u5c0f\u578b\u795e\u7ecf\u7f51\u7edc\u6a21\u5757，\u4e5f\u5c31\u662f\u6211\u4eec\u6240\u8bf4\u7684\u9002\u914d\u5668（Adapters）\u3002\u8fd9\u4e9b\u9002\u914d\u5668\u5c31\u50cf\u662f\u6a21\u578b\u7684 \u201c\u667a\u80fd\u52a9\u624b\u201d，\u5728\u5fae\u8c03\u8fc7\u7a0b\u4e2d，\u5b83\u4eec\u80a9\u8d1f\u8d77\u5b66\u4e60\u7279\u5b9a\u4efb\u52a1\u77e5\u8bc6\u7684\u91cd\u4efb，\u800c\u6a21\u578b\u7684\u4e3b\u4f53\u53c2\u6570\u5219\u88ab\u51bb\u7ed3，\u4fdd\u6301\u4e0d\u53d8\u3002<\/p>\n

    \u4ee5 Transformer \u67b6\u6784\u7684\u5927\u6a21\u578b\u4e3a\u4f8b，\u9002\u914d\u5668\u901a\u5e38\u88ab\u63d2\u5165\u5230\u591a\u5934\u6ce8\u610f\u529b\u673a\u5236\u4e4b\u540e\u4ee5\u53ca\u524d\u9988\u7f51\u7edc\u5c42\u4e4b\u540e\u3002\u6bcf\u4e2a\u9002\u914d\u5668\u6a21\u5757\u7531\u4e24\u4e2a\u524d\u9988\u5b50\u5c42\u6784\u6210，\u7b2c\u4e00\u4e2a\u524d\u9988\u5b50\u5c42\u5c31\u50cf\u4e00\u4e2a \u201c\u538b\u7f29\u5668\u201d，\u5b83\u5c06 Transformer \u5757\u8f93\u51fa\u7684\u9ad8\u7ef4\u6570\u636e\u6295\u5f71\u5230\u4e00\u4e2a\u8f83\u4f4e\u7ef4\u5ea6\u7684\u7a7a\u95f4，\u4ece\u800c\u5927\u5927\u51cf\u5c11\u4e86\u53c2\u6570\u7684\u6570\u91cf；\u63a5\u7740，\u901a\u8fc7\u4e00\u4e2a\u975e\u7ebf\u6027\u6fc0\u6d3b\u51fd\u6570，\u4e3a\u6a21\u578b\u5f15\u5165\u975e\u7ebf\u6027\u56e0\u7d20，\u589e\u5f3a\u5176\u8868\u8fbe\u80fd\u529b；\u6700\u540e，\u7b2c\u4e8c\u4e2a\u524d\u9988\u5b50\u5c42\u5c06\u6570\u636e\u7ef4\u5ea6\u6062\u590d\u5230\u539f\u59cb\u5927\u5c0f，\u4f5c\u4e3a\u9002\u914d\u5668\u7684\u8f93\u51fa\u3002\u8fd9\u79cd\u72ec\u7279\u7684\u8bbe\u8ba1，\u4f7f\u5f97\u9002\u914d\u5668\u80fd\u591f\u5728\u4e0d\u6539\u53d8\u6a21\u578b\u4e3b\u4f53\u7ed3\u6784\u7684\u524d\u63d0\u4e0b，\u9ad8\u6548\u5730\u5b66\u4e60\u7279\u5b9a\u4efb\u52a1\u7684\u77e5\u8bc6，\u5b9e\u73b0\u5bf9\u6a21\u578b\u7684\u5fae\u8c03\u3002<\/p>\n

    （\u4e8c）\u64cd\u4f5c\u65b9\u6cd5<\/h4>\n
  • \n

    \u63d2\u5165\u9002\u914d\u5668：\u5728 Transformer \u5c42\u7684\u7279\u5b9a\u4f4d\u7f6e，\u5982\u591a\u5934\u6ce8\u610f\u529b\u673a\u5236\u540e\u7684\u8f93\u51fa\u4ee5\u53ca\u524d\u9988\u7f51\u7edc\u5c42\u540e\u7684\u8f93\u51fa，\u6dfb\u52a0\u9002\u914d\u5668\u6a21\u5757\u3002\u8fd9\u4e00\u8fc7\u7a0b\u5c31\u50cf\u662f\u5728\u4e00\u6761\u751f\u4ea7\u7ebf\u4e0a，\u5de7\u5999\u5730\u589e\u8bbe\u4e86\u4e24\u4e2a\u7279\u6b8a\u7684 \u201c\u52a0\u5de5\u73af\u8282\u201d，\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u66f4\u6709\u9488\u5bf9\u6027\u7684\u5904\u7406\u3002<\/p>\n<\/li>\n

  • \n

    \u8bad\u7ec3\u9002\u914d\u5668：\u5728\u8bad\u7ec3\u9636\u6bb5，\u5c06\u6a21\u578b\u4e3b\u4f53\u7684\u53c2\u6570\u51bb\u7ed3，\u4f7f\u5176\u4e0d\u518d\u66f4\u65b0，\u5c31\u5982\u540c\u5c06\u4e00\u5ea7\u5df2\u7ecf\u5efa\u597d\u7684\u5927\u53a6\u56fa\u5b9a\u4f4f，\u4e0d\u518d\u8fdb\u884c\u5927\u89c4\u6a21\u7684\u6539\u9020\u3002\u7136\u540e，\u4f7f\u7528\u4f18\u5316\u5668（\u5982 Adam \u4f18\u5316\u5668）\u4ec5\u5bf9\u9002\u914d\u5668\u7684\u53c2\u6570\u8fdb\u884c\u8bad\u7ec3\u3002\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d，\u9002\u914d\u5668\u4f1a\u6839\u636e\u8f93\u5165\u7684\u6570\u636e\u548c\u4efb\u52a1\u76ee\u6807，\u4e0d\u65ad\u8c03\u6574\u81ea\u8eab\u7684\u53c2\u6570，\u5b66\u4e60\u7279\u5b9a\u4efb\u52a1\u7684\u6a21\u5f0f\u548c\u89c4\u5f8b，\u4ece\u800c\u4f7f\u6a21\u578b\u80fd\u591f\u66f4\u597d\u5730\u9002\u5e94\u65b0\u7684\u4efb\u52a1\u3002<\/p>\n<\/li>\n

    （\u4e09）\u5e94\u7528\u573a\u666f<\/h4>\n

    Adapter Tuning \u9002\u7528\u4e8e\u591a\u79cd\u573a\u666f，\u5c24\u5176\u662f\u5728\u591a\u4efb\u52a1\u5b66\u4e60\u548c\u8de8\u8bed\u8a00\u8fc1\u79fb\u7b49\u9886\u57df\u8868\u73b0\u51fa\u8272\u3002\u5728\u591a\u4efb\u52a1\u5b66\u4e60\u4e2d，\u53ef\u4ee5\u4e3a\u6bcf\u4e2a\u4efb\u52a1\u8bad\u7ec3\u4e00\u4e2a\u72ec\u7acb\u7684\u9002\u914d\u5668，\u8fd9\u4e9b\u9002\u914d\u5668\u5171\u4eab\u540c\u4e00\u4e2a\u9884\u8bad\u7ec3\u6a21\u578b，\u5c31\u50cf\u4e0d\u540c\u7684\u4e58\u5ba2\u4e58\u5750\u540c\u4e00\u8f86\u516c\u5171\u6c7d\u8f66，\u5404\u81ea\u524d\u5f80\u4e0d\u540c\u7684\u76ee\u7684\u5730\u3002\u8fd9\u6837，\u6a21\u578b\u53ef\u4ee5\u5728\u4e0d\u540c\u4efb\u52a1\u4e4b\u95f4\u5feb\u901f\u5207\u6362，\u63d0\u9ad8\u4e86\u6a21\u578b\u7684\u901a\u7528\u6027\u548c\u6548\u7387\u3002\u5728\u8de8\u8bed\u8a00\u8fc1\u79fb\u4efb\u52a1\u4e2d，Adapter Tuning \u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u5feb\u901f\u9002\u5e94\u4e0d\u540c\u8bed\u8a00\u7684\u7279\u70b9\u548c\u8bed\u6cd5\u89c4\u5219，\u5c06\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u77e5\u8bc6\u8fc1\u79fb\u5230\u4f4e\u8d44\u6e90\u8bed\u8a00\u7684\u4efb\u52a1\u4e2d，\u5b9e\u73b0\u8bed\u8a00\u4e4b\u95f4\u7684\u65e0\u7f1d\u5207\u6362\u548c\u77e5\u8bc6\u5171\u4eab\u3002<\/p>\n

    （\u56db）\u6027\u80fd\u4f18\u52bf<\/h4>\n
  • \n

    \u51cf\u5c11\u8ba1\u7b97\u8d44\u6e90\u9700\u6c42：\u7531\u4e8e\u53ea\u9700\u8bad\u7ec3\u5c11\u91cf\u7684\u9002\u914d\u5668\u53c2\u6570，\u800c\u65e0\u9700\u66f4\u65b0\u6574\u4e2a\u6a21\u578b\u7684\u53c2\u6570，Adapter Tuning \u5927\u5927\u964d\u4f4e\u4e86\u8ba1\u7b97\u6210\u672c\u548c\u5185\u5b58\u9700\u6c42\u3002\u8fd9\u4f7f\u5f97\u5728\u8d44\u6e90\u6709\u9650\u7684\u60c5\u51b5\u4e0b，\u4e5f\u80fd\u8f7b\u677e\u5bf9\u5927\u6a21\u578b\u8fdb\u884c\u5fae\u8c03，\u5c31\u50cf\u7528\u4e00\u8f86\u5c0f\u578b\u6c7d\u8f66\u5b8c\u6210\u4e86\u5927\u578b\u5361\u8f66\u624d\u80fd\u5b8c\u6210\u7684\u90e8\u5206\u4efb\u52a1，\u65e2\u8282\u7701\u4e86\u80fd\u6e90，\u53c8\u63d0\u9ad8\u4e86\u6548\u7387\u3002<\/p>\n<\/li>\n

  • \n

    \u65b9\u4fbf\u4efb\u52a1\u5207\u6362：\u6bcf\u4e2a\u4efb\u52a1\u90fd\u53ef\u4ee5\u6709\u81ea\u5df1\u72ec\u7acb\u7684\u9002\u914d\u5668，\u5e76\u4e14\u8fd9\u4e9b\u9002\u914d\u5668\u53ef\u4ee5\u65b9\u4fbf\u5730\u63d2\u5165\u6216\u79fb\u9664，\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u5feb\u901f\u9002\u5e94\u4e0d\u540c\u7684\u4efb\u52a1\u9700\u6c42\u3002\u8fd9\u5c31\u597d\u6bd4\u4e00\u4e2a\u591a\u529f\u80fd\u5de5\u5177，\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u7684\u5de5\u4f5c\u9700\u6c42，\u5feb\u901f\u66f4\u6362\u4e0d\u540c\u7684\u914d\u4ef6，\u5b9e\u73b0\u4e0d\u540c\u7684\u529f\u80fd，\u63d0\u9ad8\u4e86\u6a21\u578b\u7684\u7075\u6d3b\u6027\u548c\u9002\u5e94\u6027\u3002<\/p>\n<\/li>\n

    P-Tuning：\u7075\u6d3b\u63d0\u793a<\/h3>\n

    （\u4e00）\u6280\u672f\u6838\u5fc3<\/h4>\n

    P-Tuning \u4f5c\u4e3a\u4e00\u79cd\u521b\u65b0\u7684\u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f，\u5176\u6838\u5fc3\u5728\u4e8e\u5728\u8f93\u5165\u6587\u672c\u4e2d\u5de7\u5999\u5730\u63d2\u5165\u53ef\u8bad\u7ec3\u7684\u865a\u62df token，\u8fd9\u4e9b\u865a\u62df token \u5c31\u50cf\u662f\u4e3a\u6a21\u578b\u91cf\u8eab\u5b9a\u5236\u7684 \u201c\u5c0f\u52a9\u624b\u201d，\u80fd\u591f\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u7406\u89e3\u548c\u5904\u7406\u4e0b\u6e38\u4efb\u52a1\u3002\u4e0e\u4f20\u7edf\u7684\u79bb\u6563\u578b\u63d0\u793a\u8bcd\u4e0d\u540c，P-Tuning \u5141\u8bb8\u5728\u8fde\u7eed\u7a7a\u95f4\u4e2d\u8fdb\u884c\u68af\u5ea6\u66f4\u65b0\u64cd\u4f5c，\u8fd9\u4e0d\u4ec5\u6781\u5927\u5730\u63d0\u5347\u4e86\u6a21\u578b\u7684\u6548\u679c，\u8fd8\u663e\u8457\u589e\u5f3a\u4e86\u5176\u5728\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u9c81\u68d2\u6027\u548c\u4e00\u81f4\u6027\u3002<\/p>\n

    （\u4e8c）\u5b9e\u73b0\u6d41\u7a0b<\/h4>\n
  • \n

    \u786e\u5b9a\u865a\u62df token \u4f4d\u7f6e\u548c\u6570\u91cf：\u6839\u636e\u5177\u4f53\u4efb\u52a1\u548c\u6a21\u578b\u7ed3\u6784，\u7cbe\u5fc3\u786e\u5b9a\u865a\u62df token \u7684\u63d2\u5165\u4f4d\u7f6e\u548c\u6570\u91cf\u3002\u901a\u5e38，\u865a\u62df token \u4f1a\u88ab\u653e\u7f6e\u5728\u6807\u51c6\u8f93\u5165\u4e4b\u524d\u6216\u8005\u4e2d\u95f4\u4f4d\u7f6e\u3002\u4ee5\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e3a\u4f8b，\u5047\u8bbe\u6211\u4eec\u4f7f\u7528\u7684\u662f BERT \u6a21\u578b，\u9996\u5148\u9700\u8981\u786e\u5b9a\u5728\u8f93\u5165\u5e8f\u5217\u7684\u54ea\u4e2a\u4f4d\u7f6e\u63d2\u5165\u865a\u62df token\u3002\u5982\u679c\u5e0c\u671b\u6a21\u578b\u5728\u5904\u7406\u6587\u672c\u524d\u5c31\u83b7\u53d6\u7279\u5b9a\u4efb\u52a1\u7684\u63d0\u793a\u4fe1\u606f，\u53ef\u4ee5\u5c06\u865a\u62df token \u63d2\u5165\u5230\u8f93\u5165\u5e8f\u5217\u7684\u5f00\u5934；\u5982\u679c\u60f3\u8981\u6a21\u578b\u5728\u5bf9\u6587\u672c\u6709\u4e00\u5b9a\u7406\u89e3\u540e\u518d\u7ed3\u5408\u63d0\u793a\u8fdb\u884c\u5224\u65ad，\u4e5f\u53ef\u4ee5\u5c06\u865a\u62df token \u63d2\u5165\u5230\u6587\u672c\u4e2d\u95f4\u7684\u9002\u5f53\u4f4d\u7f6e\u3002\u81f3\u4e8e\u6570\u91cf，\u4e00\u822c\u901a\u8fc7\u5b9e\u9a8c\u6765\u786e\u5b9a，\u5e38\u89c1\u7684\u53d6\u503c\u8303\u56f4\u5728 5 – 20 \u4e4b\u95f4\u3002\u4f8b\u5982，\u5728\u4e00\u4e2a\u60c5\u611f\u5206\u6790\u4efb\u52a1\u4e2d，\u7ecf\u8fc7\u591a\u6b21\u5b9e\u9a8c\u53d1\u73b0，\u63d2\u5165 10 \u4e2a\u865a\u62df token \u65f6，\u6a21\u578b\u7684\u5206\u7c7b\u51c6\u786e\u7387\u6700\u9ad8\u3002<\/p>\n<\/li>\n

  • \n

    \u8bad\u7ec3\u865a\u62df token：\u901a\u8fc7\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5\u5bf9\u865a\u62df token \u7684\u5d4c\u5165\u5411\u91cf\u8fdb\u884c\u4f18\u5316，\u4ee5\u4f7f\u5176\u66f4\u597d\u5730\u9002\u5e94\u4efb\u52a1\u9700\u6c42\u3002\u4e0b\u9762\u662f\u4e00\u6bb5\u4f7f\u7528 PyTorch \u5b9e\u73b0 P-Tuning \u8bad\u7ec3\u7684\u7b80\u5355\u4ee3\u7801\u793a\u4f8b：<\/p>\n<\/li>\n

    import torch<\/p>\n

    from transformers import AutoModelForSequenceClassification, AutoTokenizer<\/p>\n

    \\\\# \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u5206\u8bcd\u5668<\/p>\n

    model\\\\_name = 'bert-base-uncased'<\/p>\n

    model = AutoModelForSequenceClassification.from\\\\_pretrained(model\\\\_name)<\/p>\n

    tokenizer = AutoTokenizer.from\\\\_pretrained(model\\\\_name)<\/p>\n

    \\\\# \u5b9a\u4e49\u865a\u62dftoken\u7684\u6570\u91cf\u548c\u5d4c\u5165\u7ef4\u5ea6<\/p>\n

    num\\\\_virtual\\\\_tokens = 10<\/p>\n

    embedding\\\\_dim = model.config.hidden\\\\_size<\/p>\n

    \\\\# \u521d\u59cb\u5316\u865a\u62dftoken\u7684\u5d4c\u5165\u5411\u91cf<\/p>\n

    virtual\\\\_embeddings = torch.nn.Parameter(torch.randn(num\\\\_virtual\\\\_tokens, embedding\\\\_dim))<\/p>\n

    \\\\# \u5b9a\u4e49\u4f18\u5316\u5668<\/p>\n

    optimizer = torch.optim.Adam(\\\\[virtual\\\\_embeddings], lr=1e-3)<\/p>\n

    \\\\# \u5047\u8bbe\u5df2\u7ecf\u51c6\u5907\u597d\u7684\u8f93\u5165\u6587\u672c\u548c\u6807\u7b7e<\/p>\n

    input\\\\_texts = \\\\["\u8fd9\u662f\u4e00\u90e8\u5f88\u68d2\u7684\u7535\u5f71", "\u8fd9\u4e2a\u4ea7\u54c1\u8d28\u91cf\u5f88\u5dee"]<\/p>\n

    labels = \\\\[1, 0]<\/p>\n

    for epoch in range(3):<\/p>\n

    for input\\\\_text, label in zip(input\\\\_texts, labels):<\/p>\n

    optimizer.zero\\\\_grad()<\/p>\n

    \\\\# \u5bf9\u8f93\u5165\u6587\u672c\u8fdb\u884c\u7f16\u7801<\/p>\n

    inputs = tokenizer(input\\\\_text, return\\\\_tensors='pt')<\/p>\n

    input\\\\_ids = inputs\\\\['input\\\\_ids']<\/p>\n

    attention\\\\_mask = inputs\\\\['attention\\\\_mask']<\/p>\n

    \\\\# \u5c06\u865a\u62dftoken\u7684\u5d4c\u5165\u5411\u91cf\u4e0e\u8f93\u5165\u6587\u672c\u7684\u5d4c\u5165\u5411\u91cf\u62fc\u63a5<\/p>\n

    virtual\\\\_embeds = virtual\\\\_embeddings.unsqueeze(0).repeat(input\\\\_ids.size(0), 1, 1)<\/p>\n

    new\\\\_input\\\\_embeds = torch.cat(\\\\[virtual\\\\_embeds, model.get\\\\_input\\\\_embeddings()(input\\\\_ids)], dim=1)<\/p>\n

    \\\\# \u6269\u5c55attention\\\\_mask<\/p>\n

    new\\\\_attention\\\\_mask = torch.cat(\\\\[torch.ones(input\\\\_ids.size(0), num\\\\_virtual\\\\_tokens), attention\\\\_mask], dim=1)<\/p>\n

    \\\\# \u524d\u5411\u4f20\u64ad<\/p>\n

    outputs = model(inputs\\\\_embeds=new\\\\_input\\\\_embeds, attention\\\\_mask=new\\\\_attention\\\\_mask)<\/p>\n

    logits = outputs.logits<\/p>\n

    \\\\# \u8ba1\u7b97\u635f\u5931<\/p>\n

    loss = torch.nn.functional.cross\\\\_entropy(logits, torch.tensor(\\\\[label]))<\/p>\n

    loss.backward()<\/p>\n

    optimizer.step()<\/p>\n

    （\u4e09）\u5e94\u7528\u9886\u57df<\/h4>\n

    P-Tuning \u51ed\u501f\u5176\u9ad8\u6548\u6027\u53ca\u666e\u9002\u6027\u5f3a\u7684\u7279\u70b9，\u5728\u591a\u4e2a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u65b9\u5411\u4e0a\u90fd\u5f97\u5230\u4e86\u5e7f\u6cdb\u5e94\u7528\u3002\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d，\u501f\u52a9\u7cbe\u5fc3\u8bbe\u8ba1\u7684 prompt \u7ed3\u6784，\u5373\u4f7f\u9762\u5bf9\u7c7b\u522b\u5206\u5e03\u6781\u5ea6\u4e0d\u5e73\u8861\u7684\u6570\u636e\u96c6，\u4e5f\u80fd\u53d6\u5f97\u4e0d\u9519\u7684\u7ed3\u679c\u3002\u4f8b\u5982，\u5728\u5bf9\u65b0\u95fb\u6587\u7ae0\u8fdb\u884c\u5206\u7c7b\u65f6，P-Tuning \u80fd\u591f\u51c6\u786e\u5730\u5c06\u6587\u7ae0\u5206\u7c7b\u5230\u653f\u6cbb\u3001\u7ecf\u6d4e\u3001\u5a31\u4e50\u7b49\u4e0d\u540c\u7c7b\u522b\u4e2d；\u5728\u547d\u540d\u5b9e\u4f53\u8bc6\u522b（NER）\u4efb\u52a1\u4e2d，\u901a\u8fc7\u5bf9\u53e5\u5b50\u5185\u90e8\u5173\u7cfb\u5efa\u6a21，\u8fdb\u4e00\u6b65\u6316\u6398\u6f5c\u5728\u89c4\u5f8b，\u63d0\u9ad8\u6807\u6ce8\u7cbe\u5ea6，\u5e2e\u52a9\u8bc6\u522b\u51fa\u6587\u672c\u4e2d\u7684\u4eba\u540d\u3001\u5730\u540d\u3001\u7ec4\u7ec7\u673a\u6784\u540d\u7b49\u5b9e\u4f53；\u5728\u673a\u5668\u7ffb\u8bd1（MT）\u4efb\u52a1\u4e2d，\u4f5c\u4e3a\u6e90\u8bed\u8a00\u4fa7\u8865\u5145\u6750\u6599，\u8f85\u52a9\u7406\u89e3\u590d\u6742\u53e5\u6cd5\u73b0\u8c61，\u63d0\u5347\u7ffb\u8bd1\u7684\u51c6\u786e\u6027\u548c\u6d41\u7545\u6027\u3002<\/p>\n

    （\u56db）\u72ec\u7279\u4e4b\u5904<\/h4>\n
  • \n

    \u7075\u6d3b\u8c03\u6574\u63d0\u793a：P-Tuning \u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u7684\u4efb\u52a1\u9700\u6c42，\u7075\u6d3b\u5730\u8c03\u6574\u865a\u62df token \u7684\u5185\u5bb9\u548c\u4f4d\u7f6e，\u4ece\u800c\u4f7f\u6a21\u578b\u80fd\u591f\u66f4\u597d\u5730\u9002\u5e94\u5404\u79cd\u590d\u6742\u4efb\u52a1\u3002\u8fd9\u5c31\u597d\u6bd4\u4e00\u4e2a\u4e07\u80fd\u7684\u5de5\u5177，\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u7684\u5de5\u4f5c\u9700\u6c42，\u968f\u65f6\u66f4\u6362\u4e0d\u540c\u7684\u914d\u4ef6，\u4ee5\u5b8c\u6210\u5404\u79cd\u4efb\u52a1\u3002<\/p>\n<\/li>\n

  • \n

    \u5bf9\u6a21\u578b\u7ed3\u6784\u6539\u52a8\u8f83\u5c0f：\u4e0e\u5176\u4ed6\u4e00\u4e9b\u9700\u8981\u5bf9\u6a21\u578b\u7ed3\u6784\u8fdb\u884c\u8f83\u5927\u6539\u52a8\u7684\u5fae\u8c03\u65b9\u6cd5\u76f8\u6bd4，P-Tuning \u53ea\u9700\u8981\u5728\u8f93\u5165\u4e2d\u63d2\u5165\u865a\u62df token，\u5bf9\u6a21\u578b\u7684\u4e3b\u4f53\u7ed3\u6784\u51e0\u4e4e\u6ca1\u6709\u5f71\u54cd，\u8fd9\u4f7f\u5f97\u5b83\u5728\u5e94\u7528\u65f6\u66f4\u52a0\u4fbf\u6377，\u4e5f\u51cf\u5c11\u4e86\u56e0\u6a21\u578b\u7ed3\u6784\u6539\u53d8\u800c\u5e26\u6765\u7684\u6f5c\u5728\u98ce\u9669\u3002<\/p>\n<\/li>\n

    P-Tuning v2：\u4f18\u5316\u5347\u7ea7<\/h3>\n

    （\u4e00）\u6539\u8fdb\u70b9\u5206\u6790<\/h4>\n

    P-Tuning v2 \u4f5c\u4e3a P-Tuning \u7684\u4f18\u5316\u5347\u7ea7\u7248，\u5728\u591a\u4e2a\u5173\u952e\u65b9\u9762\u8fdb\u884c\u4e86\u91cd\u5927\u6539\u8fdb\u3002\u5728\u63d0\u793a\u4f18\u5316\u65b9\u9762，P-Tuning v2 \u91c7\u7528\u4e86\u6df1\u5ea6\u63d0\u793a\u4f18\u5316\u7b56\u7565，\u4e0d\u518d\u5c40\u9650\u4e8e\u4ec5\u5728 Transformer \u7684\u7b2c\u4e00\u5c42\u8f93\u5165\u5d4c\u5165\u5e8f\u5217\u4e2d\u63d2\u5165\u524d\u7f00\u5411\u91cf，\u800c\u662f\u5728\u6bcf\u4e00\u5c42 Transformer \u4e2d\u90fd\u52a0\u5165\u4e86 Prompts tokens \u4f5c\u4e3a\u524d\u7f00\u8f93\u5165\u3002\u8fd9\u4e00\u6539\u8fdb\u4f7f\u5f97\u53ef\u8bad\u7ec3\u7684\u53c2\u6570\u5927\u5e45\u589e\u591a，\u6a21\u578b\u80fd\u591f\u66f4\u5145\u5206\u5730\u5229\u7528\u63d0\u793a\u4fe1\u606f，\u4ece\u800c\u5728\u5e94\u5bf9\u590d\u6742\u7684\u81ea\u7136\u8bed\u8a00\u7406\u89e3（NLU）\u4efb\u52a1\u65f6，\u5c55\u73b0\u51fa\u6bd4\u539f\u59cb P-Tuning \u66f4\u4e3a\u51fa\u8272\u7684\u6027\u80fd\u3002<\/p>\n

    \u5728\u8bad\u7ec3\u65b9\u5f0f\u4e0a，P-Tuning v2 \u5f15\u5165\u4e86\u591a\u9636\u6bb5\u8bad\u7ec3\u673a\u5236\u3002\u5728\u4e0d\u540c\u7684\u8bad\u7ec3\u9636\u6bb5，\u6a21\u578b\u6709\u7740\u4e0d\u540c\u7684\u8bad\u7ec3\u76ee\u6807\u548c\u91cd\u70b9，\u901a\u8fc7\u9010\u6b65\u4f18\u5316，\u80fd\u591f\u66f4\u597d\u5730\u5b66\u4e60\u4efb\u52a1\u76f8\u5173\u7684\u77e5\u8bc6\u548c\u6a21\u5f0f，\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u548c\u9002\u5e94\u6027\u3002\u540c\u65f6，P-Tuning v2 \u8fd8\u652f\u6301\u52a8\u6001\u8c03\u6574\u63d0\u793a，\u6839\u636e\u4efb\u52a1\u7684\u8fdb\u5c55\u548c\u6570\u636e\u7684\u7279\u70b9，\u7075\u6d3b\u5730\u6539\u53d8\u63d0\u793a\u7684\u5185\u5bb9\u548c\u5f62\u5f0f，\u4f7f\u6a21\u578b\u80fd\u591f\u66f4\u7cbe\u51c6\u5730\u805a\u7126\u4e8e\u4efb\u52a1\u9700\u6c42，\u63d0\u5347\u5fae\u8c03\u6548\u679c\u3002<\/p>\n

    （\u4e8c）\u64cd\u4f5c\u8981\u70b9<\/h4>\n
  • \n

    \u591a\u9636\u6bb5\u8bad\u7ec3：\u5728\u521d\u59cb\u9636\u6bb5，\u4e3b\u8981\u76ee\u6807\u662f\u8ba9\u6a21\u578b\u5feb\u901f\u9002\u5e94\u4efb\u52a1\u7684\u5927\u81f4\u65b9\u5411，\u901a\u8fc7\u5927\u91cf\u7684\u901a\u7528\u6570\u636e\u8fdb\u884c\u521d\u6b65\u8bad\u7ec3，\u4f7f\u6a21\u578b\u5bf9\u4efb\u52a1\u6709\u4e00\u4e2a\u57fa\u672c\u7684\u8ba4\u77e5\u3002\u5728\u8fd9\u4e2a\u9636\u6bb5，\u5b66\u4e60\u7387\u53ef\u4ee5\u8bbe\u7f6e\u5f97\u76f8\u5bf9\u8f83\u5927，\u4ee5\u52a0\u5feb\u6a21\u578b\u7684\u6536\u655b\u901f\u5ea6\u3002\u4f8b\u5982，\u5b66\u4e60\u7387\u53ef\u8bbe\u4e3a 0.001，\u8fdb\u884c 5 – 10 \u4e2a epoch \u7684\u8bad\u7ec3\u3002<\/p>\n

    \u968f\u7740\u8bad\u7ec3\u7684\u63a8\u8fdb，\u8fdb\u5165\u7cbe\u7ec6\u8c03\u6574\u9636\u6bb5，\u6b64\u65f6\u6a21\u578b\u5df2\u7ecf\u5bf9\u4efb\u52a1\u6709\u4e86\u4e00\u5b9a\u7684\u7406\u89e3，\u9700\u8981\u8fdb\u4e00\u6b65\u4f18\u5316\u6a21\u578b\u7684\u6027\u80fd，\u4f7f\u5176\u80fd\u591f\u66f4\u51c6\u786e\u5730\u5b8c\u6210\u4efb\u52a1\u3002\u8fd9\u4e00\u9636\u6bb5\u53ef\u4ee5\u964d\u4f4e\u5b66\u4e60\u7387，\u5982\u8c03\u6574\u4e3a 0.0001，\u5e76\u4f7f\u7528\u66f4\u5177\u9488\u5bf9\u6027\u7684\u6570\u636e\u8fdb\u884c\u8bad\u7ec3，\u8bad\u7ec3 epoch \u6570\u53ef\u6839\u636e\u4efb\u52a1\u7684\u590d\u6742\u7a0b\u5ea6\u548c\u6a21\u578b\u7684\u6536\u655b\u60c5\u51b5\u8fdb\u884c\u8c03\u6574，\u4e00\u822c\u5728 10 – 20 \u4e2a epoch \u5de6\u53f3\u3002<\/p>\n

    \u5728\u6700\u540e\u7684\u5de9\u56fa\u9636\u6bb5，\u6a21\u578b\u4e3b\u8981\u662f\u5bf9\u4e4b\u524d\u5b66\u4e60\u5230\u7684\u77e5\u8bc6\u8fdb\u884c\u5de9\u56fa\u548c\u5f3a\u5316，\u9632\u6b62\u8fc7\u62df\u5408\u3002\u53ef\u4ee5\u4f7f\u7528\u9a8c\u8bc1\u96c6\u6570\u636e\u8fdb\u884c\u5fae\u8c03，\u5b66\u4e60\u7387\u8fdb\u4e00\u6b65\u964d\u4f4e，\u5982\u8bbe\u4e3a 0.00001，\u8bad\u7ec3 5 – 10 \u4e2a epoch\u3002<\/p>\n<\/li>\n

  • \n

    \u52a8\u6001\u8c03\u6574\u63d0\u793a：\u6839\u636e\u4efb\u52a1\u7684\u7c7b\u578b\u548c\u7279\u70b9，\u9009\u62e9\u5408\u9002\u7684\u63d0\u793a\u7ed3\u6784\u548c\u5185\u5bb9\u3002\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d，\u53ef\u4ee5\u4f7f\u7528 \u201c\u8bf7\u5224\u65ad\u4ee5\u4e0b\u6587\u672c\u5c5e\u4e8e [\u7c7b\u522b 1]\u3001[\u7c7b\u522b 2] \u8fd8\u662f [\u7c7b\u522b 3]：[\u6587\u672c\u5185\u5bb9]\u201d \u8fd9\u6837\u7684\u63d0\u793a\u7ed3\u6784；\u5728\u95ee\u7b54\u4efb\u52a1\u4e2d，\u53ef\u4ee5\u91c7\u7528 \u201c\u95ee\u9898：[\u95ee\u9898\u5185\u5bb9]，\u8bf7\u56de\u7b54：\u201d \u7684\u5f62\u5f0f\u3002<\/p>\n

    \u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u6839\u636e\u6a21\u578b\u7684\u53cd\u9988\u548c\u8bc4\u4f30\u6307\u6807，\u5b9e\u65f6\u8c03\u6574\u63d0\u793a\u3002\u5982\u679c\u53d1\u73b0\u6a21\u578b\u5728\u67d0\u4e2a\u7c7b\u522b\u4e0a\u7684\u5206\u7c7b\u51c6\u786e\u7387\u8f83\u4f4e，\u53ef\u4ee5\u9488\u5bf9\u6027\u5730\u4fee\u6539\u63d0\u793a，\u589e\u52a0\u4e0e\u8be5\u7c7b\u522b\u76f8\u5173\u7684\u5f15\u5bfc\u4fe1\u606f；\u5982\u679c\u6a21\u578b\u5728\u56de\u7b54\u95ee\u9898\u65f6\u51fa\u73b0\u903b\u8f91\u9519\u8bef，\u53ef\u4ee5\u8c03\u6574\u63d0\u793a，\u5f15\u5bfc\u6a21\u578b\u66f4\u52a0\u6ce8\u91cd\u903b\u8f91\u63a8\u7406\u3002<\/p>\n<\/li>\n

    （\u4e09）\u9002\u7528\u573a\u666f<\/h4>\n

    P-Tuning v2 \u7279\u522b\u9002\u7528\u4e8e\u590d\u6742\u4efb\u52a1\u7684\u5fae\u8c03，\u5982\u591a\u8df3\u63a8\u7406\u4efb\u52a1\u3002\u5728\u8fd9\u7c7b\u4efb\u52a1\u4e2d，\u6a21\u578b\u9700\u8981\u7efc\u5408\u591a\u4e2a\u6b65\u9aa4\u7684\u63a8\u7406\u624d\u80fd\u5f97\u51fa\u6b63\u786e\u7684\u7ed3\u8bba，P-Tuning v2 \u7684\u6df1\u5ea6\u63d0\u793a\u4f18\u5316\u548c\u591a\u9636\u6bb5\u8bad\u7ec3\u673a\u5236，\u80fd\u591f\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u7406\u89e3\u548c\u5904\u7406\u590d\u6742\u7684\u63a8\u7406\u8fc7\u7a0b，\u63d0\u9ad8\u63a8\u7406\u7684\u51c6\u786e\u6027\u3002\u5728\u9605\u8bfb\u7406\u89e3\u4efb\u52a1\u4e2d，\u5f53\u9700\u8981\u56de\u7b54\u9700\u8981\u7efc\u5408\u5206\u6790\u6587\u7ae0\u591a\u4e2a\u6bb5\u843d\u5185\u5bb9\u7684\u590d\u6742\u95ee\u9898\u65f6，P-Tuning v2 \u53ef\u4ee5\u901a\u8fc7\u52a8\u6001\u8c03\u6574\u63d0\u793a，\u5f15\u5bfc\u6a21\u578b\u5173\u6ce8\u5173\u952e\u4fe1\u606f，\u4ece\u800c\u66f4\u51c6\u786e\u5730\u56de\u7b54\u95ee\u9898\u3002<\/p>\n

    （\u56db）\u4f18\u52bf\u4f53\u73b0<\/h4>\n
  • \n

    \u66f4\u5f3a\u7684\u4efb\u52a1\u9002\u5e94\u80fd\u529b：\u901a\u8fc7\u591a\u9636\u6bb5\u8bad\u7ec3\u548c\u52a8\u6001\u63d0\u793a\u8c03\u6574，P-Tuning v2 \u80fd\u591f\u66f4\u597d\u5730\u9002\u5e94\u5404\u79cd\u590d\u6742\u4efb\u52a1\u7684\u9700\u6c42，\u63d0\u5347\u6a21\u578b\u5728\u8fd9\u4e9b\u4efb\u52a1\u4e0a\u7684\u8868\u73b0\u3002\u4e0e\u5176\u4ed6\u5fae\u8c03\u65b9\u6cd5\u76f8\u6bd4，P-Tuning v2 \u5728\u5904\u7406\u591a\u8df3\u63a8\u7406\u4efb\u52a1\u65f6，\u51c6\u786e\u7387\u53ef\u63d0\u9ad8 10% – 20%\u3002<\/p>\n<\/li>\n

  • \n

    \u66f4\u597d\u5730\u5229\u7528\u6a21\u578b\u7684\u4e0a\u4e0b\u6587\u7406\u89e3\u80fd\u529b：\u5728\u6bcf\u4e00\u5c42 Transformer \u4e2d\u90fd\u52a0\u5165\u63d0\u793a\u4fe1\u606f，\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u66f4\u5145\u5206\u5730\u5229\u7528\u4e0a\u4e0b\u6587\u4fe1\u606f，\u589e\u5f3a\u5bf9\u6587\u672c\u7684\u7406\u89e3\u548c\u5206\u6790\u80fd\u529b\u3002\u5728\u8bed\u4e49\u7406\u89e3\u4efb\u52a1\u4e2d，P-Tuning v2 \u80fd\u591f\u66f4\u51c6\u786e\u5730\u6355\u6349\u6587\u672c\u4e2d\u7684\u8bed\u4e49\u5173\u7cfb，\u4ece\u800c\u63d0\u9ad8\u4efb\u52a1\u7684\u5b8c\u6210\u8d28\u91cf \u3002<\/p>\n<\/li>\n

    \u4e03\u3001\u65b9\u6cd5\u5bf9\u6bd4\u4e0e\u9009\u62e9\u7b56\u7565<\/h3>\n

    （\u4e00）\u6027\u80fd\u5bf9\u6bd4<\/h4>\n
  • \n

    \u8ba1\u7b97\u8d44\u6e90\u9700\u6c42：\u5168\u53c2\u6570\u5fae\u8c03\u5bf9\u8ba1\u7b97\u8d44\u6e90\u7684\u9700\u6c42\u6700\u4e3a\u5e9e\u5927，\u56e0\u4e3a\u5b83\u9700\u8981\u66f4\u65b0\u6a21\u578b\u7684\u6240\u6709\u53c2\u6570，\u5728\u5904\u7406\u5927\u89c4\u6a21\u6a21\u578b\u65f6，\u53ef\u80fd\u9700\u8981\u914d\u5907\u591a\u4e2a\u9ad8\u6027\u80fd GPU \u7684\u8ba1\u7b97\u96c6\u7fa4，\u4ee5\u63d0\u4f9b\u8db3\u591f\u7684\u8ba1\u7b97\u80fd\u529b\u548c\u5185\u5b58\u652f\u6301；\u800c LoRA\u3001Adapter Tuning \u7b49\u65b9\u6cd5，\u7531\u4e8e\u53ea\u9700\u8bad\u7ec3\u5c11\u91cf\u7684\u989d\u5916\u53c2\u6570（\u5982\u4f4e\u79e9\u77e9\u9635\u6216\u9002\u914d\u5668\u53c2\u6570），\u8ba1\u7b97\u8d44\u6e90\u9700\u6c42\u5927\u5e45\u964d\u4f4e，\u666e\u901a\u7684\u5355 GPU \u670d\u52a1\u5668\u5373\u53ef\u6ee1\u8db3\u9700\u6c42；Prompt Tuning\u3001Prefix Tuning \u548c P-Tuning \u7cfb\u5217\u65b9\u6cd5\u5219\u51e0\u4e4e\u4e0d\u589e\u52a0\u989d\u5916\u7684\u8ba1\u7b97\u8d1f\u62c5，\u5b83\u4eec\u4e3b\u8981\u901a\u8fc7\u8c03\u6574\u8f93\u5165\u63d0\u793a\u6216\u524d\u7f00\u5411\u91cf\u6765\u5b9e\u73b0\u5fae\u8c03，\u5bf9\u786c\u4ef6\u8d44\u6e90\u7684\u8981\u6c42\u6700\u4f4e\u3002<\/p>\n<\/li>\n

  • \n

    \u8bad\u7ec3\u65f6\u95f4：\u5168\u53c2\u6570\u5fae\u8c03\u7684\u8bad\u7ec3\u65f6\u95f4\u901a\u5e38\u8f83\u957f，\u5c24\u5176\u662f\u5bf9\u4e8e\u5927\u89c4\u6a21\u6a21\u578b\u548c\u590d\u6742\u4efb\u52a1，\u53ef\u80fd\u9700\u8981\u6570\u5929\u751a\u81f3\u6570\u5468\u7684\u65f6\u95f4\u624d\u80fd\u5b8c\u6210\u8bad\u7ec3；LoRA \u548c Adapter Tuning \u7531\u4e8e\u8bad\u7ec3\u53c2\u6570\u8f83\u5c11，\u8bad\u7ec3\u65f6\u95f4\u76f8\u5bf9\u8f83\u77ed，\u4e00\u822c\u53ef\u4ee5\u5728\u51e0\u5c0f\u65f6\u5230\u51e0\u5929\u5185\u5b8c\u6210；Prompt Tuning\u3001Prefix Tuning \u548c P-Tuning \u7cfb\u5217\u65b9\u6cd5\u7684\u8bad\u7ec3\u65f6\u95f4\u6781\u77ed，\u56e0\u4e3a\u5b83\u4eec\u4e0d\u6d89\u53ca\u6a21\u578b\u4e3b\u4f53\u53c2\u6570\u7684\u66f4\u65b0，\u53ea\u9700\u5bf9\u63d0\u793a\u6216\u524d\u7f00\u5411\u91cf\u8fdb\u884c\u4f18\u5316，\u901a\u5e38\u53ef\u4ee5\u5728\u51e0\u5206\u949f\u5230\u51e0\u5c0f\u65f6\u5185\u5b8c\u6210\u8bad\u7ec3\u3002<\/p>\n<\/li>\n

  • \n

    \u6a21\u578b\u7cbe\u5ea6：\u5728\u7406\u60f3\u60c5\u51b5\u4e0b，\u5168\u53c2\u6570\u5fae\u8c03\u80fd\u591f\u5145\u5206\u6316\u6398\u6a21\u578b\u7684\u6f5c\u529b，\u5728\u7279\u5b9a\u4efb\u52a1\u4e0a\u83b7\u5f97\u6700\u9ad8\u7684\u7cbe\u5ea6；LoRA \u548c Adapter Tuning \u5728\u5927\u591a\u6570\u4efb\u52a1\u4e0a\u4e5f\u80fd\u53d6\u5f97\u8f83\u597d\u7684\u7cbe\u5ea6，\u4e0e\u5168\u53c2\u6570\u5fae\u8c03\u7684\u5dee\u8ddd\u8f83\u5c0f，\u7279\u522b\u662f\u5728\u6570\u636e\u91cf\u5145\u8db3\u7684\u60c5\u51b5\u4e0b；Prompt Tuning\u3001Prefix Tuning \u548c P-Tuning \u7cfb\u5217\u65b9\u6cd5\u5728\u4e00\u4e9b\u7b80\u5355\u4efb\u52a1\u6216\u96f6\u6837\u672c \/ \u5c11\u6837\u672c\u5b66\u4e60\u573a\u666f\u4e2d\u8868\u73b0\u51fa\u8272，\u4f46\u5728\u590d\u6742\u4efb\u52a1\u4e0a，\u7531\u4e8e\u5b83\u4eec\u4e0d\u6539\u53d8\u6a21\u578b\u7684\u6838\u5fc3\u53c2\u6570，\u7cbe\u5ea6\u53ef\u80fd\u76f8\u5bf9\u8f83\u4f4e\u3002<\/p>\n<\/li>\n

  • \n

    \u6cdb\u5316\u80fd\u529b：\u5168\u53c2\u6570\u5fae\u8c03\u5982\u679c\u8bad\u7ec3\u6570\u636e\u4e0d\u8db3，\u5bb9\u6613\u51fa\u73b0\u8fc7\u62df\u5408\u73b0\u8c61，\u5bfc\u81f4\u6cdb\u5316\u80fd\u529b\u4e0b\u964d；LoRA \u548c Adapter Tuning \u901a\u8fc7\u5f15\u5165\u5c11\u91cf\u7684\u989d\u5916\u53c2\u6570\u8fdb\u884c\u8bad\u7ec3，\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u51cf\u5c11\u4e86\u8fc7\u62df\u5408\u7684\u98ce\u9669，\u6cdb\u5316\u80fd\u529b\u76f8\u5bf9\u8f83\u597d；Prompt Tuning\u3001Prefix Tuning \u548c P-Tuning \u7cfb\u5217\u65b9\u6cd5\u7531\u4e8e\u4e0d\u6539\u53d8\u6a21\u578b\u7684\u4e3b\u4f53\u7ed3\u6784，\u5bf9\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u5f71\u54cd\u8f83\u5c0f，\u5728\u8de8\u4efb\u52a1\u548c\u8de8\u9886\u57df\u7684\u5e94\u7528\u4e2d\u5177\u6709\u4e00\u5b9a\u7684\u4f18\u52bf\u3002<\/p>\n<\/li>\n

    （\u4e8c）\u8d44\u6e90\u9002\u914d<\/h4>\n
  • \n

    GPU \u663e\u5b58\u5145\u8db3\u4e14\u8ba1\u7b97\u80fd\u529b\u5f3a：\u5982\u679c\u62e5\u6709\u914d\u5907\u591a\u4e2a\u9ad8\u6027\u80fd GPU \u7684\u8ba1\u7b97\u96c6\u7fa4，\u4e14\u663e\u5b58\u5145\u8db3，\u8ba1\u7b97\u80fd\u529b\u5f3a\u5927，\u53ef\u4ee5\u9009\u62e9\u5168\u53c2\u6570\u5fae\u8c03，\u4ee5\u5145\u5206\u53d1\u6325\u6a21\u578b\u7684\u6027\u80fd，\u83b7\u5f97\u6700\u4f73\u7684\u7cbe\u5ea6；\u4e5f\u53ef\u4ee5\u5c1d\u8bd5\u4e00\u4e9b\u5bf9\u8ba1\u7b97\u8d44\u6e90\u8981\u6c42\u8f83\u9ad8\u7684\u9ad8\u7ea7\u4f18\u5316\u65b9\u6cd5，\u5982\u7ed3\u5408\u5206\u5e03\u5f0f\u8bad\u7ec3\u6280\u672f，\u8fdb\u4e00\u6b65\u52a0\u901f\u8bad\u7ec3\u8fc7\u7a0b\u3002<\/p>\n<\/li>\n

  • \n

    \u4e2d\u7b49\u8ba1\u7b97\u8d44\u6e90：\u5bf9\u4e8e\u62e5\u6709\u5355 GPU \u6216\u5c11\u91cf GPU \u7684\u670d\u52a1\u5668，\u4e14\u663e\u5b58\u548c\u8ba1\u7b97\u80fd\u529b\u5904\u4e8e\u4e2d\u7b49\u6c34\u5e73，LoRA\u3001Adapter Tuning \u662f\u6bd4\u8f83\u5408\u9002\u7684\u9009\u62e9\u3002\u5b83\u4eec\u80fd\u591f\u5728\u6709\u9650\u7684\u8d44\u6e90\u4e0b，\u6709\u6548\u5730\u5bf9\u6a21\u578b\u8fdb\u884c\u5fae\u8c03，\u5728\u8ba1\u7b97\u8d44\u6e90\u548c\u6a21\u578b\u6027\u80fd\u4e4b\u95f4\u53d6\u5f97\u8f83\u597d\u7684\u5e73\u8861；\u4e5f\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u91cf\u5316\u6280\u672f，\u5982 4 \u4f4d\u6216 8 \u4f4d\u91cf\u5316，\u8fdb\u4e00\u6b65\u51cf\u5c11\u6a21\u578b\u7684\u5185\u5b58\u5360\u7528\u548c\u8ba1\u7b97\u9700\u6c42\u3002<\/p>\n<\/li>\n

  • \n

    \u8d44\u6e90\u6709\u9650：\u5728\u8d44\u6e90\u975e\u5e38\u6709\u9650\u7684\u60c5\u51b5\u4e0b，\u5982\u4f7f\u7528\u6d88\u8d39\u7ea7\u663e\u5361\u6216\u8fb9\u7f18\u8bbe\u5907，Prompt Tuning\u3001Prefix Tuning \u548c P-Tuning \u7cfb\u5217\u65b9\u6cd5\u662f\u9996\u9009\u3002\u8fd9\u4e9b\u65b9\u6cd5\u51e0\u4e4e\u4e0d\u589e\u52a0\u989d\u5916\u7684\u8ba1\u7b97\u8d1f\u62c5，\u80fd\u591f\u5728\u4f4e\u8d44\u6e90\u73af\u5883\u4e0b\u5b9e\u73b0\u6a21\u578b\u7684\u5fae\u8c03，\u6ee1\u8db3\u4e00\u4e9b\u7b80\u5355\u4efb\u52a1\u7684\u9700\u6c42；\u8fd8\u53ef\u4ee5\u901a\u8fc7\u4f18\u5316\u6570\u636e\u5904\u7406\u6d41\u7a0b\u548c\u6a21\u578b\u67b6\u6784，\u8fdb\u4e00\u6b65\u964d\u4f4e\u8d44\u6e90\u6d88\u8017\u3002<\/p>\n<\/li>\n

    （\u4e09）\u4efb\u52a1\u5339\u914d<\/h4>\n
  • \n

    \u6587\u672c\u5206\u7c7b\u4efb\u52a1：\u5982\u679c\u6570\u636e\u91cf\u5145\u8db3，\u4e14\u5bf9\u6a21\u578b\u7cbe\u5ea6\u8981\u6c42\u6781\u9ad8，\u5168\u53c2\u6570\u5fae\u8c03\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9；\u5982\u679c\u6570\u636e\u91cf\u6709\u9650，\u6216\u8005\u5e0c\u671b\u5728\u6709\u9650\u7684\u8d44\u6e90\u4e0b\u5feb\u901f\u5b8c\u6210\u5fae\u8c03，LoRA\u3001Adapter Tuning \u53ef\u4ee5\u5728\u4fdd\u8bc1\u4e00\u5b9a\u7cbe\u5ea6\u7684\u524d\u63d0\u4e0b，\u63d0\u9ad8\u5fae\u8c03\u6548\u7387；Prompt Tuning\u3001Prefix Tuning \u548c P-Tuning \u7cfb\u5217\u65b9\u6cd5\u5728\u96f6\u6837\u672c\u6216\u5c11\u6837\u672c\u7684\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\u5177\u6709\u72ec\u7279\u7684\u4f18\u52bf，\u53ef\u4ee5\u901a\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1\u63d0\u793a，\u8ba9\u6a21\u578b\u5728\u51e0\u4e4e\u6ca1\u6709\u6837\u672c\u7684\u60c5\u51b5\u4e0b\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n<\/li>\n

  • \n

    \u6587\u672c\u751f\u6210\u4efb\u52a1：Prefix Tuning \u5728\u6587\u672c\u751f\u6210\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u8272，\u5b83\u80fd\u591f\u901a\u8fc7\u8c03\u6574\u524d\u7f00\u5411\u91cf，\u6709\u6548\u5730\u63a7\u5236\u6a21\u578b\u7684\u8f93\u51fa\u65b9\u5411，\u751f\u6210\u7b26\u5408\u7279\u5b9a\u98ce\u683c\u548c\u4e3b\u9898\u7684\u6587\u672c；LoRA \u548c Adapter Tuning \u4e5f\u53ef\u4ee5\u7528\u4e8e\u6587\u672c\u751f\u6210\u4efb\u52a1，\u901a\u8fc7\u8bad\u7ec3\u5c11\u91cf\u7684\u53c2\u6570，\u8ba9\u6a21\u578b\u5b66\u4e60\u5230\u7279\u5b9a\u7684\u751f\u6210\u6a21\u5f0f；\u5168\u53c2\u6570\u5fae\u8c03\u5728\u6570\u636e\u91cf\u5145\u8db3\u4e14\u5bf9\u751f\u6210\u8d28\u91cf\u8981\u6c42\u6781\u9ad8\u7684\u60c5\u51b5\u4e0b，\u53ef\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6548\u679c\u3002<\/p>\n<\/li>\n

  • \n

    \u95ee\u7b54\u4efb\u52a1：\u5bf9\u4e8e\u9700\u8981\u51c6\u786e\u56de\u7b54\u95ee\u9898\u7684\u4efb\u52a1，\u5168\u53c2\u6570\u5fae\u8c03\u53ef\u4ee5\u8ba9\u6a21\u578b\u5145\u5206\u5b66\u4e60\u95ee\u9898\u548c\u7b54\u6848\u4e4b\u95f4\u7684\u5173\u7cfb，\u63d0\u9ad8\u56de\u7b54\u7684\u51c6\u786e\u6027；LoRA \u548c Adapter Tuning \u5728\u8d44\u6e90\u6709\u9650\u7684\u60c5\u51b5\u4e0b，\u80fd\u591f\u5feb\u901f\u5730\u5bf9\u6a21\u578b\u8fdb\u884c\u5fae\u8c03，\u4ee5\u9002\u5e94\u4e0d\u540c\u9886\u57df\u7684\u95ee\u7b54\u4efb\u52a1；P-Tuning \u7cfb\u5217\u65b9\u6cd5\u5728\u5904\u7406\u590d\u6742\u7684\u591a\u8df3\u63a8\u7406\u95ee\u7b54\u4efb\u52a1\u65f6，\u901a\u8fc7\u6df1\u5ea6\u63d0\u793a\u4f18\u5316\u548c\u591a\u9636\u6bb5\u8bad\u7ec3\u673a\u5236，\u80fd\u591f\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u7406\u89e3\u95ee\u9898\u548c\u63a8\u7406\u7b54\u6848，\u63d0\u9ad8\u56de\u7b54\u7684\u8d28\u91cf\u3002<\/p>\n<\/li>\n

    \u5b9e\u8df5\u6848\u4f8b\u5206\u4eab<\/h3>\n

    （\u4e00）\u533b\u7597\u9886\u57df\u6848\u4f8b<\/h4>\n

    \u5728\u533b\u7597\u9886\u57df，\u75be\u75c5\u8bca\u65ad\u7684\u51c6\u786e\u6027\u76f4\u63a5\u5173\u7cfb\u5230\u60a3\u8005\u7684\u751f\u547d\u5065\u5eb7\u548c\u6cbb\u7597\u6548\u679c\u3002\u4e00\u5bb6\u533b\u7597\u79d1\u6280\u516c\u53f8\u5728\u8fdb\u884c\u75be\u75c5\u8bca\u65ad\u6a21\u578b\u7684\u7814\u53d1\u65f6，\u91c7\u7528\u4e86\u5168\u53c2\u6570\u5fae\u8c03\u7684\u65b9\u6cd5\u3002\u4ed6\u4eec\u57fa\u4e8e\u9884\u8bad\u7ec3\u7684 BERT \u6a21\u578b，\u4f7f\u7528\u4e86\u5927\u91cf\u7684\u533b\u5b66\u6587\u732e\u3001\u75c5\u4f8b\u6570\u636e\u4ee5\u53ca\u4e13\u5bb6\u6807\u6ce8\u7684\u8bca\u65ad\u7ed3\u679c\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002<\/p>\n

    \u5728\u6570\u636e\u51c6\u5907\u9636\u6bb5，\u7814\u7a76\u4eba\u5458\u7cbe\u5fc3\u6536\u96c6\u4e86\u6db5\u76d6\u591a\u79cd\u75be\u75c5\u7c7b\u578b\u3001\u4e0d\u540c\u75c7\u72b6\u8868\u73b0\u548c\u8bca\u65ad\u7ed3\u679c\u7684\u75c5\u4f8b\u6570\u636e，\u5171\u8ba1 5 \u4e07\u6761\u3002\u8fd9\u4e9b\u6570\u636e\u6765\u81ea\u591a\u5bb6\u5927\u578b\u533b\u9662，\u5177\u6709\u5e7f\u6cdb\u7684\u4ee3\u8868\u6027\u3002\u7136\u540e，\u5bf9\u6570\u636e\u8fdb\u884c\u4e86\u7ec6\u81f4\u7684\u6e05\u6d17\u548c\u9884\u5904\u7406，\u5305\u62ec\u53bb\u9664\u566a\u58f0\u6570\u636e\u3001\u7edf\u4e00\u6570\u636e\u683c\u5f0f\u3001\u5bf9\u533b\u5b66\u672f\u8bed\u8fdb\u884c\u6807\u51c6\u5316\u5904\u7406\u7b49，\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u4e00\u81f4\u6027\u3002<\/p>\n

    \u5728\u5fae\u8c03\u8fc7\u7a0b\u4e2d，\u4f7f\u7528\u4e86 AdamW \u4f18\u5316\u5668，\u8bbe\u7f6e\u5b66\u4e60\u7387\u4e3a 5e-5，\u8bad\u7ec3\u8f6e\u6570\u4e3a 10 \u8f6e\u3002\u7ecf\u8fc7\u5168\u53c2\u6570\u5fae\u8c03\u540e\u7684\u6a21\u578b，\u5728\u75be\u75c5\u8bca\u65ad\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u7684\u6548\u679c\u3002\u4e0e\u672a\u5fae\u8c03\u7684\u6a21\u578b\u76f8\u6bd4，\u51c6\u786e\u7387\u4ece 70% \u63d0\u5347\u5230\u4e86 85%，\u53ec\u56de\u7387\u4e5f\u4ece 65% \u63d0\u9ad8\u5230\u4e86 80%\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d，\u8be5\u6a21\u578b\u80fd\u591f\u5feb\u901f\u51c6\u786e\u5730\u5206\u6790\u60a3\u8005\u7684\u75c7\u72b6\u63cf\u8ff0\u548c\u68c0\u67e5\u62a5\u544a，\u4e3a\u533b\u751f\u63d0\u4f9b\u66f4\u5177\u53c2\u8003\u4ef7\u503c\u7684\u8bca\u65ad\u5efa\u8bae，\u5927\u5927\u63d0\u9ad8\u4e86\u75be\u75c5\u8bca\u65ad\u7684\u6548\u7387\u548c\u51c6\u786e\u6027，\u4e3a\u60a3\u8005\u7684\u53ca\u65f6\u6cbb\u7597\u63d0\u4f9b\u4e86\u6709\u529b\u652f\u6301\u3002<\/p>\n

    （\u4e8c）\u91d1\u878d\u9886\u57df\u6848\u4f8b<\/h4>\n

    \u4e00\u5bb6\u91d1\u878d\u673a\u6784\u5728\u8fdb\u884c\u91d1\u878d\u98ce\u9669\u9884\u6d4b\u4efb\u52a1\u65f6，\u5c1d\u8bd5\u4e86\u591a\u79cd\u5fae\u8c03\u65b9\u6cd5，\u5e76\u5bf9\u5176\u5e94\u7528\u6548\u679c\u8fdb\u884c\u4e86\u6df1\u5165\u5206\u6790\u3002\u4ed6\u4eec\u9996\u5148\u4f7f\u7528\u4e86\u5168\u53c2\u6570\u5fae\u8c03\u65b9\u6cd5，\u57fa\u4e8e\u9884\u8bad\u7ec3\u7684\u91d1\u878d\u9886\u57df\u5927\u6a21\u578b，\u4f7f\u7528\u4e86\u8fc7\u53bb 10 \u5e74\u7684\u5386\u53f2\u4ea4\u6613\u6570\u636e\u3001\u5e02\u573a\u6ce2\u52a8\u6570\u636e\u4ee5\u53ca\u5b8f\u89c2\u7ecf\u6d4e\u6307\u6807\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\u3002\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u7531\u4e8e\u6570\u636e\u91cf\u5e9e\u5927，\u8ba1\u7b97\u8d44\u6e90\u6d88\u8017\u5de8\u5927，\u8bad\u7ec3\u65f6\u95f4\u957f\u8fbe\u4e00\u5468\u3002\u867d\u7136\u6700\u7ec8\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u8868\u73b0\u51fa\u8272，\u51c6\u786e\u7387\u8fbe\u5230\u4e86 90%，\u4f46\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u51c6\u786e\u7387\u4ec5\u4e3a 75%，\u51fa\u73b0\u4e86\u660e\u663e\u7684\u8fc7\u62df\u5408\u73b0\u8c61\u3002<\/p>\n

    \u968f\u540e，\u4ed6\u4eec\u91c7\u7528\u4e86 LoRA \u5fae\u8c03\u65b9\u6cd5\u3002\u5728 Transformer \u5c42\u7684\u6ce8\u610f\u529b\u673a\u5236\u90e8\u5206\u63d2\u5165\u4e86\u4f4e\u79e9\u77e9\u9635，\u79e9 r \u8bbe\u7f6e\u4e3a 16，\u7f29\u653e\u56e0\u5b50 alpha \u8bbe\u4e3a 32，Dropout \u6982\u7387\u8bbe\u4e3a 0.2\u3002\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u4ec5\u5bf9\u4f4e\u79e9\u77e9\u9635\u7684\u53c2\u6570\u8fdb\u884c\u66f4\u65b0，\u51bb\u7ed3\u4e86\u539f\u59cb\u6a21\u578b\u7684\u6743\u91cd\u3002\u8fd9\u6837\u4e00\u6765，\u8ba1\u7b97\u8d44\u6e90\u9700\u6c42\u5927\u5e45\u964d\u4f4e，\u8bad\u7ec3\u65f6\u95f4\u7f29\u77ed\u81f3\u4e24\u5929\u3002\u5728\u6d4b\u8bd5\u96c6\u4e0a，\u6a21\u578b\u7684\u51c6\u786e\u7387\u8fbe\u5230\u4e86 80%，\u867d\u7136\u7565\u4f4e\u4e8e\u5168\u53c2\u6570\u5fae\u8c03\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u8868\u73b0，\u4f46\u6cdb\u5316\u80fd\u529b\u660e\u663e\u589e\u5f3a，\u6709\u6548\u5730\u51cf\u5c11\u4e86\u8fc7\u62df\u5408\u73b0\u8c61\u3002<\/p>\n

    \u901a\u8fc7\u8fd9\u4e2a\u6848\u4f8b\u53ef\u4ee5\u770b\u51fa，\u5728\u91d1\u878d\u98ce\u9669\u9884\u6d4b\u4efb\u52a1\u4e2d，\u4e0d\u540c\u7684\u5fae\u8c03\u65b9\u6cd5\u5404\u6709\u4f18\u52a3\u3002\u5168\u53c2\u6570\u5fae\u8c03\u867d\u7136\u5728\u6570\u636e\u5145\u8db3\u65f6\u53ef\u80fd\u83b7\u5f97\u8f83\u9ad8\u7684\u7cbe\u5ea6，\u4f46\u8ba1\u7b97\u6210\u672c\u9ad8，\u5bb9\u6613\u8fc7\u62df\u5408；\u800c LoRA \u5fae\u8c03\u65b9\u6cd5\u5219\u5728\u8ba1\u7b97\u8d44\u6e90\u6709\u9650\u7684\u60c5\u51b5\u4e0b，\u80fd\u591f\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u5e73\u8861\u8ba1\u7b97\u6210\u672c\u548c\u6a21\u578b\u6027\u80fd，\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b \u3002\u91d1\u878d\u673a\u6784\u5728\u9009\u62e9\u5fae\u8c03\u65b9\u6cd5\u65f6，\u9700\u8981\u7efc\u5408\u8003\u8651\u81ea\u8eab\u7684\u8d44\u6e90\u60c5\u51b5\u3001\u6570\u636e\u7279\u70b9\u4ee5\u53ca\u4efb\u52a1\u9700\u6c42，\u4ee5\u9009\u62e9\u6700\u5408\u9002\u7684\u5fae\u8c03\u7b56\u7565\u3002<\/p>\n

    \u603b\u7ed3\u4e0e\u5c55\u671b<\/h3>\n

    （\u4e00）\u56de\u987e\u8981\u70b9<\/h4>\n

    \u5728\u5927\u6a21\u578b\u5fae\u8c03\u7684\u9886\u57df\u4e2d，\u6211\u4eec\u63a2\u7d22\u4e86\u4e03\u79cd\u5404\u5177\u7279\u8272\u7684\u5fae\u8c03\u65b9\u6cd5\u3002\u5168\u53c2\u6570\u5fae\u8c03\u901a\u8fc7\u66f4\u65b0\u6a21\u578b\u7684\u6240\u6709\u53c2\u6570，\u80fd\u591f\u5145\u5206\u6316\u6398\u6a21\u578b\u6f5c\u529b，\u5728\u6570\u636e\u5145\u8db3\u65f6\u83b7\u5f97\u9ad8\u7cbe\u5ea6，\u4f46\u8ba1\u7b97\u8d44\u6e90\u9700\u6c42\u5927，\u8bad\u7ec3\u65f6\u95f4\u957f，\u4e14\u5bb9\u6613\u8fc7\u62df\u5408；LoRA \u5f15\u5165\u4f4e\u79e9\u77e9\u9635，\u663e\u8457\u51cf\u5c11\u8ba1\u7b97\u91cf\u548c\u5185\u5b58\u9700\u6c42，\u80fd\u5feb\u901f\u9002\u914d\u591a\u4e2a\u4efb\u52a1，\u4f46\u5728\u590d\u6742\u4efb\u52a1\u4e0a\u53ef\u80fd\u5b58\u5728\u6027\u80fd\u635f\u5931；Prompt Tuning \u901a\u8fc7\u4f18\u5316\u8f93\u5165\u63d0\u793a\u6765\u8c03\u6574\u6a21\u578b\u884c\u4e3a，\u8bad\u7ec3\u6210\u672c\u4f4e，\u90e8\u7f72\u7b80\u5355，\u4f46\u5bf9\u7279\u5b9a\u4efb\u52a1\u7684\u9002\u914d\u80fd\u529b\u6709\u9650；Prefix Tuning \u5728\u8f93\u5165\u524d\u6dfb\u52a0\u53ef\u8bad\u7ec3\u524d\u7f00\u5411\u91cf，\u6709\u6548\u63a7\u5236\u6a21\u578b\u8f93\u51fa\u65b9\u5411，\u8ba1\u7b97\u8d44\u6e90\u6d88\u8017\u5c11，\u4f46\u524d\u7f00\u8bbe\u8ba1\u548c\u8c03\u6574\u8f83\u4e3a\u5173\u952e；Adapter Tuning \u5728\u6a21\u578b\u5c42\u95f4\u63d2\u5165\u9002\u914d\u5668，\u51cf\u5c11\u8ba1\u7b97\u8d44\u6e90\u9700\u6c42，\u65b9\u4fbf\u4efb\u52a1\u5207\u6362，\u9002\u7528\u4e8e\u591a\u4efb\u52a1\u5b66\u4e60\u548c\u8de8\u8bed\u8a00\u8fc1\u79fb；P-Tuning \u5728\u8f93\u5165\u6587\u672c\u4e2d\u63d2\u5165\u53ef\u8bad\u7ec3\u865a\u62df token，\u7075\u6d3b\u8c03\u6574\u63d0\u793a，\u5bf9\u6a21\u578b\u7ed3\u6784\u6539\u52a8\u5c0f，\u9002\u7528\u4e8e\u591a\u79cd\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1；P-Tuning v2 \u901a\u8fc7\u6df1\u5ea6\u63d0\u793a\u4f18\u5316\u548c\u591a\u9636\u6bb5\u8bad\u7ec3\u673a\u5236，\u589e\u5f3a\u4e86\u6a21\u578b\u5bf9\u590d\u6742\u4efb\u52a1\u7684\u9002\u5e94\u80fd\u529b，\u80fd\u66f4\u597d\u5730\u5229\u7528\u6a21\u578b\u7684\u4e0a\u4e0b\u6587\u7406\u89e3\u80fd\u529b \u3002<\/p>\n

    （\u4e8c）\u672a\u6765\u8d8b\u52bf<\/h4>\n

    \u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f\u672a\u6765\u5c06\u671d\u7740\u4e0e\u5176\u4ed6\u6280\u672f\u6df1\u5ea6\u878d\u5408\u7684\u65b9\u5411\u53d1\u5c55\u3002\u4e0e\u77e5\u8bc6\u56fe\u8c31\u6280\u672f\u7ed3\u5408，\u80fd\u591f\u4e3a\u6a21\u578b\u63d0\u4f9b\u66f4\u4e30\u5bcc\u7684\u80cc\u666f\u77e5\u8bc6，\u4f7f\u5176\u5728\u63a8\u7406\u548c\u56de\u7b54\u95ee\u9898\u65f6\u66f4\u52a0\u51c6\u786e\u548c\u5168\u9762；\u4e0e\u5f3a\u5316\u5b66\u4e60\u76f8\u7ed3\u5408，\u53ef\u4ee5\u901a\u8fc7\u4e0d\u65ad\u7684\u4ea4\u4e92\u548c\u53cd\u9988，\u8fdb\u4e00\u6b65\u4f18\u5316\u6a21\u578b\u7684\u884c\u4e3a，\u4f7f\u5176\u80fd\u591f\u5728\u52a8\u6001\u73af\u5883\u4e2d\u66f4\u597d\u5730\u9002\u5e94\u4efb\u52a1\u9700\u6c42\u3002<\/p>\n

    \u5728\u5fae\u8c03\u7b56\u7565\u65b9\u9762，\u5c06\u4f1a\u51fa\u73b0\u66f4\u9ad8\u6548\u3001\u66f4\u667a\u80fd\u7684\u65b9\u6cd5\u3002\u4f8b\u5982，\u81ea\u52a8\u8d85\u53c2\u6570\u8c03\u6574\u6280\u672f\u5c06\u80fd\u591f\u6839\u636e\u4efb\u52a1\u7684\u7279\u70b9\u548c\u6570\u636e\u7684\u5206\u5e03，\u81ea\u52a8\u9009\u62e9\u6700\u4f18\u7684\u8d85\u53c2\u6570，\u51cf\u5c11\u4eba\u5de5\u8c03\u53c2\u7684\u5de5\u4f5c\u91cf\u548c\u4e0d\u786e\u5b9a\u6027；\u81ea\u9002\u5e94\u5fae\u8c03\u7b56\u7565\u53ef\u4ee5\u6839\u636e\u6a21\u578b\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u8868\u73b0，\u52a8\u6001\u5730\u8c03\u6574\u5fae\u8c03\u7684\u65b9\u5f0f\u548c\u5f3a\u5ea6，\u63d0\u9ad8\u5fae\u8c03\u7684\u6548\u679c\u548c\u6548\u7387\u3002<\/p>\n

    \u6b64\u5916，\u968f\u7740\u786c\u4ef6\u6280\u672f\u7684\u4e0d\u65ad\u53d1\u5c55，\u8ba1\u7b97\u8d44\u6e90\u7684\u9650\u5236\u5c06\u9010\u6e10\u5f97\u5230\u7f13\u89e3，\u8fd9\u5c06\u4e3a\u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f\u7684\u53d1\u5c55\u63d0\u4f9b\u66f4\u5e7f\u9614\u7684\u7a7a\u95f4\u3002\u540c\u65f6，\u968f\u7740\u5bf9\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u548c\u5b89\u5168\u6027\u7684\u5173\u6ce8\u5ea6\u4e0d\u65ad\u63d0\u9ad8，\u672a\u6765\u7684\u5fae\u8c03\u6280\u672f\u4e5f\u5c06\u66f4\u52a0\u6ce8\u91cd\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\u548c\u5b89\u5168\u6027，\u786e\u4fdd\u6a21\u578b\u7684\u8f93\u51fa\u7ed3\u679c\u662f\u53ef\u7406\u89e3\u548c\u53ef\u9760\u7684 \u3002<\/p>\n

    \u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f\u7684\u672a\u6765\u5145\u6ee1\u4e86\u65e0\u9650\u7684\u53ef\u80fd\u6027，\u5b83\u5c06\u7ee7\u7eed\u63a8\u52a8\u4eba\u5de5\u667a\u80fd\u6280\u672f\u7684\u53d1\u5c55，\u4e3a\u6211\u4eec\u7684\u751f\u6d3b\u548c\u5de5\u4f5c\u5e26\u6765\u66f4\u591a\u7684\u4fbf\u5229\u548c\u521b\u65b0\u3002\u5e0c\u671b\u5927\u5bb6\u80fd\u591f\u6301\u7eed\u5173\u6ce8\u8fd9\u4e00\u9886\u57df\u7684\u53d1\u5c55，\u5171\u540c\u63a2\u7d22\u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f\u7684\u66f4\u591a\u5e94\u7528\u548c\u6f5c\u529b\u3002\u5982\u679c\u4f60\u5bf9\u5927\u6a21\u578b\u5fae\u8c03\u6280\u672f\u6709\u4efb\u4f55\u60f3\u6cd5\u6216\u7ecf\u9a8c，\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u5206\u4eab，\u8ba9\u6211\u4eec\u4e00\u8d77\u4ea4\u6d41\u8fdb\u6b65！<\/p>\n","protected":false},"excerpt":{"rendered":"

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