{"id":123688,"date":"2026-11-08T02:00:55","date_gmt":"2026-11-07T18:00:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/123688.html"},"modified":"2026-11-08T02:00:55","modified_gmt":"2026-11-07T18:00:55","slug":"%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a019%ef%bc%9a%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%be%ae%e8%b0%83%e5%85%a8%e6%94%bb%e7%95%a5-lora-qlora-%e5%ae%9e%e6%88%98","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/123688.html","title":{"rendered":"\u6df1\u5ea6\u5b66\u4e6019\uff1a\u5927\u6a21\u578b\u5fae\u8c03\u5168\u653b\u7565\u2014\u2014LoRA\/QLoRA \u5b9e\u6218"},"content":{"rendered":"

\/* MD \/ \u5bcc\u6587\u672c\u4e2d\u7684 .toc\uff08\u542b\u535a\u5ba2\u56ed\u642c\u5bb6\u7b49\u5d4c\u5957\u7ed3\u6784\uff09\uff1b.toc-box \u5728\u4fa7\u680f\uff0c\u4e0d\u53d7\u5f71\u54cd *\/
\n #content_views .toc,
\n \/* \u7f16\u8f91\u5668\u5e38\u5728\u76ee\u5f55\u524d\u540e\u63d2\u5165\u7a7a p\uff08:empty \u4ecd\u5360 20px\uff09\uff0c\u4e00\u5e76\u53bb\u6389\u907f\u514d\u9876\u7a7a\u9699 *\/
\n #content_views.markdown_views > p:empty:has(+ .toc),
\n #content_views.markdown_views > .toc + p:empty,
\n \/* \u5bcc\u6587\u672c\u65e7\u7248\u76ee\u5f55\u6807\u8bb0 *\/
\n #content_views.htmledit_views #main-toc,
\n #content_views.htmledit_views #hr-toc,
\n #content_views.htmledit_views p[id*=\\”-toc\\”] {
\n display: none !important;
\n }
\n \/* \u76ee\u5f55\u53bb\u6389\u540e\uff0c\u7d27\u8ddf\u7684\u9996\u4e2a\u6807\u9898\u4e0d\u518d\u591a\u51fa\u4e00\u5757\u4e0a\u8fb9\u8ddd *\/
\n #content_views.markdown_views > .toc + h1,
\n #content_views.markdown_views > .toc + h2,
\n #content_views.markdown_views > .toc + h3,
\n #content_views.markdown_views > .toc + h4,
\n #content_views.markdown_views > .toc + p:empty + h1,
\n #content_views.markdown_views > .toc + p:empty + h2,
\n #content_views.markdown_views > .toc + p:empty + h3,
\n #content_views.markdown_views > .toc + p:empty + h4 {
\n margin-top: 0 !important;
\n }<\/p>\n

\u4e0a\u7bc7\u7528 DQN \u8ba9\u667a\u80fd\u4f53\u5b66\u4f1a\u51b3\u7b56，\u800c\u5927\u6a21\u578b\u65f6\u4ee3\u300c\u8ba9\u6a21\u578b\u5feb\u901f\u5b66\u4f1a\u65b0\u4efb\u52a1\u300d\u6700\u9ad8\u6548\u7684\u65b9\u5f0f，\u662f LoRA \u8fd9\u7c7b\u53c2\u6570\u9ad8\u6548\u5fae\u8c03\u3002<\/p>\n

\u5f15\u8a00<\/h3>\n

\u60f3\u8ba9 LLaMA-7B \u8bf4"\u4eba\u8bdd"\u3001\u61c2\u4f60\u7684\u4e1a\u52a1，\u4f20\u7edf\u505a\u6cd5\u662f\u5168\u53c2\u6570\u5fae\u8c03\u2014\u2014\u4f46 7B \u6a21\u578b\u5168\u91cf\u5fae\u8c03\u8981\u5403 ~80GB \u663e\u5b58，\u7b49\u4e8e\u56db\u5f20 A100\u3002\u8fd9\u5bf9\u4e2a\u4eba\u548c\u4e2d\u5c0f\u56e2\u961f\u662f\u5929\u6587\u6570\u5b57\u3002\u597d\u6d88\u606f\u662f：95% \u7684\u6743\u91cd\u5176\u5b9e\u4e0d\u7528\u52a8\u3002\u53c2\u6570\u9ad8\u6548\u5fae\u8c03（PEFT）\u53ea\u8bad\u7ec3\u6781\u5c11\u53c2\u6570\u5c31\u80fd\u63a5\u8fd1\u5168\u91cf\u6548\u679c，\u5176\u4e2d LoRA \u548c QLoRA \u628a\u95e8\u69db\u538b\u5230\u4e86"\u4e00\u5f20 24G \u6d88\u8d39\u5361\u4e5f\u80fd\u73a9"\u3002\u672c\u7bc7\u624b\u628a\u624b\u5e26\u4f60\u8dd1\u901a\u3002<\/p>\n

\n

\u4e00\u3001\u5168\u53c2\u6570\u5fae\u8c03\u7684\u663e\u5b58\u74f6\u9888<\/h3>\n

\u8bad\u7ec3\u663e\u5b58 \u2248 \u6a21\u578b\u53c2\u6570 \u00d7（\u6bcf\u53c2\u6570\u5b57\u8282 + \u4f18\u5316\u5668\u72b6\u6001 + \u68af\u5ea6）\u3002FP16 \u4e0b：\u53c2\u6570 2 \u5b57\u8282\u3001\u68af\u5ea6 2 \u5b57\u8282\u3001Adam \u72b6\u6001（m,v）4 \u5b57\u8282 \u2192 \u6bcf\u4e2a\u53c2\u6570\u7ea6 8 \u5b57\u8282\u3002<\/p>\n\n\u6a21\u578b\u53c2\u6570\u91cf\u5168\u91cf\u5fae\u8c03\u663e\u5b58（\u7ea6）<\/tr>\n\n\n\n\n
7B<\/td>\n70 \u4ebf<\/td>\n~56 GB（FP16）<\/td>\n<\/tr>\n
13B<\/td>\n130 \u4ebf<\/td>\n~100 GB+<\/td>\n<\/tr>\n
70B<\/td>\n700 \u4ebf<\/td>\n\u591a\u5f20 A100<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u663e\u7136\u4e0d\u73b0\u5b9e\u3002PEFT \u7684\u601d\u8def：\u51bb\u7ed3\u57fa\u5ea7，\u53ea\u8bad\u7ec3\u65c1\u8def\u7684\u5c0f\u53c2\u6570，\u663e\u5b58\u968f\u53ef\u8bad\u7ec3\u53c2\u6570\u9aa4\u964d\u3002<\/p>\n

\n

\u4e8c\u3001LoRA \u4f4e\u79e9\u5206\u89e3\u539f\u7406<\/h3>\n

\u6838\u5fc3\u6d1e\u5bdf：\u6743\u91cd\u66f4\u65b0 \u0394W \u5728\u5fae\u8c03\u65f6\u5f80\u5f80\u662f\u4f4e\u79e9\u7684\u3002\u4e8e\u662f\u628a \u0394W \u62c6\u6210\u4e24\u4e2a\u5c0f\u77e9\u9635：<\/p>\n

h = W\u2080 x + \u0394W x = W\u2080 x + B A x
\n \u00a0 \u00a0 \u00a0 \u00a0 (d\u00d7k) \u00a0 \u00a0 (d\u00d7r)(r\u00d7k) \u00a0 \u00a0 r \u226a d,k <\/p>\n

    \n
  • \n

    W\u2080 \u51bb\u7ed3\u4e0d\u52a8（\u4e0d\u5360\u68af\u5ea6\/\u4f18\u5316\u5668\u72b6\u6001）\u3002<\/p>\n<\/li>\n

  • \n

    \u53ea\u8bad\u7ec3 A (r\u00d7k) \u548c B (d\u00d7r)，\u53ef\u8bad\u7ec3\u53c2\u6570\u91cf\u4ece d\u00d7k \u964d\u5230 r\u00d7(d+k)\u3002<\/p>\n<\/li>\n

  • \n

    \u63a8\u7406\u65f6\u53ef\u4ee5\u628a BA \u52a0\u56de W\u2080 \u5408\u5e76\u90e8\u7f72，\u96f6\u989d\u5916\u5ef6\u8fdf\u3002<\/p>\n<\/li>\n<\/ul>\n

    \u53c2\u6570\u91cf\u5bf9\u6bd4：\u4ee5 d=k=4096\u3001\u79e9 r=8 \u4e3a\u4f8b，\u539f\u6743\u91cd 1677 \u4e07，LoRA \u4ec5 8\u00d7(4096+4096)=6.5\u4e07，\u7ea6 0.4%\u3002\u5178\u578b\u914d\u7f6e r=8~64 \u5373\u53ef，\u53c2\u6570\u91cf\u5e38\u80fd\u538b\u5230\u539f\u6a21\u578b\u7684 0.1%~1%，\u6548\u679c\u5374\u63a5\u8fd1\u5168\u91cf\u3002<\/p>\n

    \n

    \u4e09\u3001QLoRA：4-bit \u91cf\u5316\u5fae\u8c03<\/h3>\n

    QLoRA \u5728 LoRA \u57fa\u7840\u4e0a\u518d\u780d\u4e00\u5200\u2014\u2014\u628a\u51bb\u7ed3\u7684\u57fa\u5ea7\u7528 4-bit NF4 \u91cf\u5316\u5b58\u4e0b\u6765，\u914d\u5408 \u5206\u9875\u4f18\u5316\u5668（Paged Optimizers） \u5728\u663e\u5b58\u5cf0\u503c\u65f6\u628a\u4f18\u5316\u5668\u72b6\u6001\u6362\u9875\u5230 CPU，\u8fdb\u4e00\u6b65\u7701\u663e\u5b58\u3002<\/p>\n

    \u6548\u679c\u60ca\u4eba：\u628a 65B \u6a21\u578b\u7684\u5fae\u8c03\u538b\u8fdb\u5355\u5f20 48G \u5361，7B \u6a21\u578b\u751a\u81f3\u80fd\u5728 24G \u6d88\u8d39\u5361（\u5982 RTX 4090） \u4e0a\u8dd1\u3002\u4ee3\u4ef7\u662f 4-bit \u5f15\u5165\u6781\u5c0f\u7cbe\u5ea6\u635f\u5931，\u4f46\u914d\u5408 LoRA \u5f80\u5f80\u53ef\u5ffd\u7565\u3002<\/p>\n

    import torch
    \nfrom transformers import AutoModelForCausalLM, BitsAndBytesConfig
    \n\u200b
    \nbnb_config = BitsAndBytesConfig(
    \n \u00a0 \u00a0load_in_4bit=True,
    \n \u00a0 \u00a0bnb_4bit_quant_type="nf4",
    \n \u00a0 \u00a0bnb_4bit_compute_dtype=torch.bfloat16,
    \n \u00a0 \u00a0bnb_4bit_use_double_quant=True, \u00a0 # \u5bf9\u91cf\u5316\u5e38\u6570\u518d\u91cf\u5316，\u66f4\u7701
    \n)
    \nmodel = AutoModelForCausalLM.from_pretrained(
    \n \u00a0 \u00a0"meta-llama\/Llama-2-7b-hf",
    \n \u00a0 \u00a0quantization_config=bnb_config,
    \n \u00a0 \u00a0device_map="auto",
    \n) <\/p>\n

    \n

    \u56db\u3001\u4e2d\u6587\u6307\u4ee4\u6570\u636e\u6784\u9020<\/h3>\n

    LoRA \u53ea\u6539"\u884c\u4e3a"，\u4e0d\u6539"\u77e5\u8bc6"，\u6240\u4ee5\u6570\u636e\u8d28\u91cf > \u6570\u636e\u91cf\u3002\u6307\u4ee4\u6570\u636e\u7edf\u4e00\u6210 alpaca \u683c\u5f0f：<\/p>\n

    {
    \n \u00a0"instruction": "\u628a\u4e0b\u9762\u7684\u53e5\u5b50\u7ffb\u8bd1\u6210\u82f1\u6587",
    \n \u00a0"input": "\u4eca\u5929\u5929\u6c14\u771f\u597d\u3002",
    \n \u00a0"output": "The weather is really nice today."
    \n} <\/p>\n

    \u5efa\u8bae：<\/p>\n

      \n
    • \n

      \u7528\u4f60\u4e1a\u52a1\u91cc\u7684\u771f\u5b9e\u95ee\u7b54\/\u6307\u4ee4，1000~5000 \u6761\u9ad8\u8d28\u91cf\u6837\u672c\u5e38\u591f\u89c1\u6548\u3002<\/p>\n<\/li>\n

    • \n

      \u6df7\u5165\u5c11\u91cf\u901a\u7528\u6307\u4ee4\u6570\u636e\u9632\u9057\u5fd8\u3002<\/p>\n<\/li>\n

    • \n

      \u53bb\u91cd\u3001\u6e05\u6d17\u683c\u5f0f，\u907f\u514d\u566a\u58f0\u4e3b\u5bfc\u4f4e\u79e9\u66f4\u65b0\u3002<\/p>\n<\/li>\n<\/ul>\n

      \n

      \u4e94\u3001\u8bad\u7ec3\u4e0e\u6743\u91cd\u5408\u5e76\u90e8\u7f72<\/h3>\n

      \u7528 Hugging Face peft \u51e0\u884c\u63a5\u5165：<\/p>\n

      from peft import LoraConfig, get_peft_model
      \n\u200b
      \nlora_config = LoraConfig(
      \n \u00a0 \u00a0r=8, \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u79e9
      \n \u00a0 \u00a0lora_alpha=16, \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u7f29\u653e，\u5e38\u53d6 2*r
      \n \u00a0 \u00a0target_modules=["q_proj","v_proj"], \u00a0# \u6ce8\u5165\u6ce8\u610f\u529b\u7684 Q\/V
      \n \u00a0 \u00a0lora_dropout=0.05,
      \n \u00a0 \u00a0bias="none",
      \n \u00a0 \u00a0task_type="CAUSAL_LM",
      \n)
      \nmodel = get_peft_model(model, lora_config)
      \nmodel.print_trainable_parameters() \u00a0 # \u4f1a\u663e\u793a\u53ef\u8bad\u7ec3\u53c2\u6570\u5360\u6bd4 ~0.1%
      \n\u200b
      \n# \u4e4b\u540e\u7528 Trainer \/ SFTTrainer \u6b63\u5e38\u8bad\u7ec3\u5373\u53ef <\/p>\n

      \u8bad\u7ec3\u5b8c\u6210\u540e\u5408\u5e76\u90e8\u7f72（\u6d88\u9664\u65c1\u8def\u5f00\u9500）：<\/p>\n

      merged = model.merge_and_unload() \u00a0 \u00a0# BA \u5408\u5e76\u56de W\u2080
      \nmerged.save_pretrained(".\/llama2-7b-lora-merged") <\/p>\n

      \u5178\u578b\u663e\u5b58\u4e0e\u6548\u679c（\u516c\u5f00\u57fa\u51c6\u533a\u95f4，\u968f\u914d\u7f6e\u6d6e\u52a8，\u975e\u672c\u673a\u5b9e\u6d4b）：<\/p>\n\n\u65b9\u68487B \u5fae\u8c03\u663e\u5b58\u6548\u679c（\u76f8\u5bf9\u5168\u91cf）<\/tr>\n\n\n\n\n
      \u5168\u53c2\u6570\u5fae\u8c03<\/td>\n~56 GB<\/td>\n100% \u57fa\u7ebf<\/td>\n<\/tr>\n
      LoRA (r=8, FP16)<\/td>\n~16 GB<\/td>\n~98%<\/td>\n<\/tr>\n
      QLoRA (4-bit)<\/td>\n~6~8 GB<\/td>\n~97%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u8fd9\u6b63\u662f"\u6d88\u8d39\u7ea7\u663e\u5361\u5b9a\u5236\u5927\u6a21\u578b"\u6210\u4e3a\u73b0\u5b9e\u7684\u539f\u56e0\u3002<\/p>\n

      \n

      \u516d\u3001\u7ecf\u9a8c\u53c2\u6570\u4e0e\u907f\u5751<\/h3>\n
    • \n

      \u79e9 r \u4e0d\u662f\u8d8a\u5927\u8d8a\u597d：r=8 \u5e38\u591f，r=64 \u672a\u5fc5\u66f4\u597d\u8fd8\u66f4\u5360\u663e\u5b58；\u5148\u5c0f\u540e\u5927\u3002<\/p>\n<\/li>\n

    • \n

      \u5b66\u4e60\u7387\u8981\u653e\u5927：LoRA \u53c2\u6570\u5c11，\u5e38\u7528 1e-4 ~ 3e-4，\u6bd4\u5168\u91cf\u5fae\u8c03（~2e-5）\u5927\u4e00\u4e2a\u91cf\u7ea7\u3002<\/p>\n<\/li>\n

    • \n

      target_modules \u9009\u5bf9：\u901a\u5e38\u6ce8\u5165 q_proj,v_proj（\u6216\u5168\u6ce8\u610f\u529b），\u522b\u6f0f\u4e86\u3002<\/p>\n<\/li>\n

    • \n

      lora_alpha \/ r \u6bd4\u503c：\u63a7\u5236\u66f4\u65b0\u5e45\u5ea6，\u5e38\u8bbe alpha = 2r\u3002<\/p>\n<\/li>\n

    • \n

      \u907f\u514d\u707e\u96be\u6027\u9057\u5fd8：\u6570\u636e\u91cc\u4fdd\u7559\u5c11\u91cf\u901a\u7528\u6837\u672c；LoRA \u672c\u8eab\u56e0\u51bb\u7ed3\u57fa\u5ea7，\u9057\u5fd8\u98ce\u9669\u5df2\u8f83\u4f4e\u3002<\/p>\n<\/li>\n

    • \n

      \u90e8\u7f72\u5408\u5e76：\u4e0a\u7ebf\u524d merge_and_unload，\u5426\u5219\u6bcf\u6b21\u63a8\u7406\u591a\u4e00\u6b21\u65c1\u8def\u8ba1\u7b97\u3002<\/p>\n<\/li>\n

      \n

      \u603b\u7ed3<\/h3>\n

      LoRA \u7528\u4f4e\u79e9\u5206\u89e3\u628a"\u5fae\u8c03\u5927\u6a21\u578b"\u4ece A100 \u96c6\u7fa4\u964d\u7ef4\u5230\u5355\u5f20\u6d88\u8d39\u5361，QLoRA \u518d\u501f 4-bit \u91cf\u5316\u628a\u95e8\u69db\u780d\u5230 24G \u663e\u5b58\u3002\u672c\u6587\u7684 PEFT \u4ee3\u7801\u53ef\u76f4\u63a5\u5957\u4f60\u7684 7B\/13B \u6a21\u578b\u2014\u2014\u8bb0\u4f4f\u94c1\u5f8b：\u6570\u636e\u8d28\u91cf\u5148\u4e8e\u53c2\u6570\u91cf，r \u5148\u5c0f\u540e\u5927，\u5b66\u4e60\u7387\u653e\u5927\u4e00\u6863\u3002\u8fd9\u662f 2026 \u5e74\u6bcf\u4e2a\u60f3\u62e5\u6709"\u4e13\u5c5e\u5927\u6a21\u578b"\u7684\u5f00\u53d1\u8005\u90fd\u8be5\u638c\u63e1\u7684\u6280\u80fd\u3002<\/p>\n

      \u53c2\u8003\u8d44\u6599<\/h3>\n
    • \n

      Hu, E., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models.<\/p>\n<\/li>\n

    • \n

      Dettmers, T., et al. (2023). QLoRA: Efficient Finetuning of Quantized LLMs.<\/p>\n<\/li>\n

    • \n

      Hugging Face PEFT \u6587\u6863：https:\/\/huggingface.co\/docs\/peft<\/p>\n<\/li>\n

    • \n

      bitsandbytes 4-bit \u91cf\u5316\u6307\u5357\u3002<\/p>\n<\/li>\n

      \u5f80\u671f\u56de\u987e<\/h3>\n
        \n
      • \n

        \u7b2c7\u7bc7：BERT \u6a21\u578b\u539f\u7406\u89e3\u6790\u4e0e\u5fae\u8c03\u5b9e\u6218<\/p>\n<\/li>\n

      • \u7b2c8\u7bc7：GPT \u7cfb\u5217\u5927\u6a21\u578b\u67b6\u6784\u6f14\u8fdb\u4e0e\u63d0\u793a\u5de5\u7a0b\u5165\u95e8<\/li>\n
      • \n

        \u591a\u6a21\u6001\u5b66\u4e60\u2014\u2014\u56fe\u6587\u8054\u5408\u8868\u5f81\u524d\u6cbf\u8fdb\u5c55<\/p>\n<\/li>\n<\/ul>\n

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