{"id":59695,"date":"2026-01-14T10:05:09","date_gmt":"2026-01-14T02:05:09","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/59695.html"},"modified":"2026-01-14T10:05:09","modified_gmt":"2026-01-14T02:05:09","slug":"%e4%b8%8d%e5%90%b9%e4%b8%8d%e9%bb%91%ef%bc%81%e8%bd%a6%e4%bc%81%e5%a4%a7%e6%a8%a1%e5%9e%8b%e9%80%89%e5%9e%8b%e5%ae%9e%e6%b5%8b%ef%bc%8c3-%e6%ac%be%e5%9f%ba%e7%a1%80%e6%a8%a1%e5%9e%8b-3-%e7%b1%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/59695.html","title":{"rendered":"\u4e0d\u5439\u4e0d\u9ed1\uff01\u8f66\u4f01\u5927\u6a21\u578b\u9009\u578b\u5b9e\u6d4b\uff0c3 \u6b3e\u57fa\u7840\u6a21\u578b + 3 \u7c7b\u7ec4\u4ef6\u771f\u5b9e\u5bf9\u6bd4"},"content":{"rendered":"

<\/h2>\n

\u505a\u5de5\u4e1a\u5927\u6a21\u578b\u9879\u76ee\uff0c\u6700\u5bb9\u6613\u8e29\u7684\u5751\u5c31\u662f \u201c\u76f2\u76ee\u5806\u6280\u672f\u201d\u2014\u2014 \u4e0a\u6765\u5c31\u9009\u6700\u5927\u53c2\u6570\u7684\u6a21\u578b\u3001\u6700\u590d\u6742\u7684\u67b6\u6784\uff0c\u6700\u540e\u8981\u4e48\u90e8\u7f72\u4e0d\u4e86\uff0c\u8981\u4e48\u6548\u679c\u4e0d\u8fbe\u6807\u3002\u5c24\u5176\u662f\u8f6c\u884c \/ \u6ca1\u9879\u76ee\u7ecf\u9a8c\u7684\u670b\u53cb\uff0c\u5f88\u5bb9\u6613\u88ab \u201c\u6280\u672f\u5671\u5934\u201d \u5e26\u504f\uff0c\u5ffd\u7565\u4e86 \u201c\u573a\u666f\u9002\u914d\u201d \u624d\u662f\u6838\u5fc3\u3002<\/p>\n

\u4e0a\u4e00\u7bc7\u6211\u4eec\u62c6\u89e3\u4e86\u8f66\u4f01\u603b\u88c5\u8f66\u95f4\u7684\u6838\u5fc3\u75db\u70b9\u548c\u6574\u4f53\u67b6\u6784\uff0c\u8fd9\u4e00\u7bc7\u5c31\u805a\u7126\u6700\u5173\u952e\u7684 \u201c\u6280\u672f\u9009\u578b\u201d \u73af\u8282 \u2014\u2014 \u6211\u4f1a\u628a\u9879\u76ee\u4e2d\u5b9e\u6d4b\u7684 3 \u6b3e\u57fa\u7840\u6a21\u578b\u30013 \u7c7b\u6838\u5fc3\u7ec4\u4ef6\uff08\u5411\u91cf\u5e93\u3001\u63a8\u7406\u6846\u67b6\u3001\u6570\u636e\u5de5\u5177\uff09\u7684\u5bf9\u6bd4\u7ed3\u679c\u5168\u76d8\u6258\u51fa\uff0c\u5305\u62ec 2 \u4ebf\u6570\u636e\u7684\u68c0\u7d22\u5ef6\u8fdf\u3001GPU \u5229\u7528\u7387\u3001\u51c6\u786e\u7387\u7b49\u786c\u6307\u6807\uff0c\u8fd8\u6709\u8e29\u8fc7\u7684 \u201c\u7ef4\u5ea6\u4e0d\u517c\u5bb9\u201d\u201c\u56fd\u4ea7\u5316\u9002\u914d\u5dee\u201d \u7b49\u5751\uff0c\u6240\u6709\u4ee3\u7801\u90fd\u80fd\u76f4\u63a5\u590d\u7528\uff0c\u5e2e\u4f60\u907f\u5f00\u9009\u578b\u8bef\u533a\u3002<\/p>\n

\u4e00\u3001\u9009\u578b\u6838\u5fc3\u539f\u5219\uff1a\u5de5\u4e1a\u573a\u666f \u201c3 \u4e0d\u9009\u201d<\/h3>\n

\u5728\u52a8\u624b\u9009\u578b\u524d\uff0c\u6211\u4eec\u5148\u660e\u786e\u4e86 3 \u4e2a\u6838\u5fc3\u539f\u5219\uff0c\u8fd9\u4e5f\u662f\u5de5\u4e1a\u573a\u666f\u548c\u4e92\u8054\u7f51\u573a\u666f\u7684\u672c\u8d28\u533a\u522b\uff0c\u8f6c\u884c\u7684\u670b\u53cb\u4e00\u5b9a\u8981\u8bb0\u7262\uff1a<\/p>\n

  • \u4e0d\u9009 \u201c\u53c2\u6570\u6700\u5927\u201d \u7684\u6a21\u578b\uff1a\u5de5\u4e1a\u573a\u666f\u8ffd\u6c42 \u201c\u7cbe\u51c6 + \u9ad8\u6548\u201d\uff0c\u4e0d\u662f \u201c\u751f\u6210\u6d41\u7545\u201d\uff0c\u5927\u53c2\u6570\u6a21\u578b\u4e0d\u4ec5\u663e\u5b58\u5360\u7528\u9ad8\uff0c\u63a8\u7406\u5ef6\u8fdf\u4e5f\u53ef\u80fd\u8d85\u6807\uff08\u6bd4\u5982 ChatGLM4-14B \u63a8\u7406\u5ef6\u8fdf\u8d85 380ms\uff0c\u4e0d\u7b26\u5408\uff1c200ms \u7684\u786c\u6307\u6807\uff09\uff1b<\/li>\n
  • \u4e0d\u9009 \u201c\u4e0d\u652f\u6301\u5206\u5e03\u5f0f\u201d \u7684\u7ec4\u4ef6\uff1a\u8f66\u95f4 3 \u5e74\u7684\u6545\u969c\u6570\u636e\u6709 2 \u4ebf\u6761\uff0c\u5355\u673a\u7ec4\u4ef6\uff08\u5982 FAISS\uff09\u5b58\u4e0d\u4e0b\uff0c\u5206\u7247\u540e\u68c0\u7d22\u5ef6\u8fdf\u4f1a\u98d9\u5347\uff1b<\/li>\n
  • \u4e0d\u9009 \u201c\u56fd\u4ea7\u5316\u9002\u914d\u5dee\u201d \u7684\u5de5\u5177\uff1a\u8f66\u4f01\u6570\u636e\u8981\u6c42\u5b58\u5185\u7f51\uff0c\u4e91\u670d\u52a1\uff08\u5982 Pinecone\uff09\u6216\u4f9d\u8d56\u6d77\u5916\u73af\u5883\u7684\u5de5\u5177\uff0c\u6839\u672c\u65e0\u6cd5\u843d\u5730\u3002<\/li>\n

    \u6240\u6709\u9009\u578b\u90fd\u56f4\u7ed5 \u201c3 \u4e2a\u786c\u6307\u6807\u201d \u5c55\u5f00\uff1a\u6545\u969c\u67e5\u8be2\u5ef6\u8fdf\uff1c200ms\u3001\u65b9\u6848\u51c6\u786e\u7387\u226590%\u3001\u6545\u969c\u6392\u67e5\u65f6\u957f\u7f29\u77ed 40%\uff0c\u6ca1\u6709\u4efb\u4f55\u6a21\u7cca\u7a7a\u95f4\u3002<\/p>\n

    \u4e8c\u3001\u57fa\u7840\u6a21\u578b\u9009\u578b\uff1a3 \u6b3e 14B \u6a21\u578b\u5b9e\u6d4b\uff0cQwen2.5-14B \u51ed\u4ec0\u4e48\u80dc\u51fa\uff1f<\/h3>\n

    \u57fa\u7840\u6a21\u578b\u662f\u9879\u76ee\u7684 \u201c\u57fa\u77f3\u201d\uff0c\u6211\u4eec\u6d4b\u8bd5\u4e86\u5f53\u524d\u4e3b\u6d41\u7684 3 \u6b3e 14B \u53c2\u6570\u6a21\u578b\uff08Qwen2.5-14B\u3001LLaMA3-14B\u3001ChatGLM4-14B\uff09\uff0c\u6ca1\u6709\u7a7a\u8c08\u7406\u8bba\uff0c\u5168\u90e8\u57fa\u4e8e\u8f66\u95f4 2 \u4ebf\u6761\u771f\u5b9e\u6545\u969c\u6570\u636e\u30011000 \u6761\u6d4b\u8bd5\u96c6\u5b9e\u6d4b\uff0c\u7ed3\u679c\u5dee\u5f02\u975e\u5e38\u660e\u663e\u3002<\/p>\n

    1. \u6838\u5fc3\u6d4b\u8bd5\u6307\u6807\uff08\u5de5\u4e1a\u573a\u666f\u91cd\u70b9\u5173\u6ce8\u8fd9 5 \u70b9\uff09<\/h4>\n
      \n
    • \u5de5\u4e1a\u672f\u8bed\u51c6\u786e\u7387\uff1a\u9488\u5bf9 200 + \u6838\u5fc3\u5de5\u4e1a\u672f\u8bed\uff08\u5982 GSD \u6587\u4ef6\u3001Profinet\u3001CRC \u6821\u9a8c\uff09\uff0c\u6d4b\u8bd5\u6a21\u578b\u89e3\u91ca\u51c6\u786e\u7387\uff1b<\/li>\n
    • \u63a8\u7406\u5ef6\u8fdf\uff1a\u5355\u6761\u8bf7\u6c42\u751f\u6210 300 \u5b57\u65b9\u6848\u7684\u5e73\u5747\u5ef6\u8fdf\uff08GPU\uff1a4 \u5361 A100\uff0c\u91cf\u5316\uff1aGPTQ 4bit\uff09\uff1b<\/li>\n
    • \u663e\u5b58\u5360\u7528\uff1a\u63a8\u7406\u65f6\u7684 GPU \u663e\u5b58\u5360\u7528\uff084 \u5361\u5f20\u91cf\u5e76\u884c\uff09\uff1b<\/li>\n
    • \u751f\u6001\u9002\u914d\u6027\uff1a\u4e0e\u5411\u91cf\u5e93\uff08Milvus\uff09\u3001\u63a8\u7406\u6846\u67b6\uff08VLLM\uff09\u7684\u517c\u5bb9\u7a0b\u5ea6\uff1b<\/li>\n
    • \u5fae\u8c03\u96be\u5ea6\uff1aLoRA \u5fae\u8c03\u65f6\u7684\u663e\u5b58\u5360\u7528\u3001\u8bad\u7ec3\u7a33\u5b9a\u6027\uff08\u662f\u5426\u51fa\u73b0\u68af\u5ea6\u7206\u70b8\u3001\u9a8c\u8bc1\u635f\u5931\u98d9\u5347\uff09\u3002<\/li>\n<\/ul>\n

      2. \u5b9e\u6d4b\u7ed3\u679c\u5bf9\u6bd4<\/h4>\n\n\n \u6d4b\u8bd5\u6307\u6807
      \n Qwen2.5-14B
      \n LLaMA3-14B
      \n ChatGLM4-14B
      \n <\/tr>\n\n\n\n\n\n\n\n\n
      \u5de5\u4e1a\u672f\u8bed\u51c6\u786e\u7387\uff08\u539f\u751f\uff09<\/td>\n93%<\/td>\n88%<\/td>\n94%<\/td>\n<\/tr>\n
      \u5de5\u4e1a\u672f\u8bed\u51c6\u786e\u7387\uff08\u5fae\u8c03\u540e\uff09<\/td>\n95%<\/td>\n91%<\/td>\n95%<\/td>\n<\/tr>\n
      \u63a8\u7406\u5ef6\u8fdf\uff08\u5355\u6761\u8bf7\u6c42\uff09<\/td>\n250ms<\/td>\n280ms<\/td>\n380ms<\/td>\n<\/tr>\n
      \u663e\u5b58\u5360\u7528\uff084 \u5361 A100\uff09<\/td>\n25GB \/ \u5361<\/td>\n28GB \/ \u5361<\/td>\n32GB \/ \u5361<\/td>\n<\/tr>\n
      \u5411\u91cf\u5e93\u9002\u914d\u6027<\/td>\n\u65e0\u7f1d\u517c\u5bb9\uff08768 \u7ef4\u5411\u91cf\uff09<\/td>\n\u4e0d\u517c\u5bb9\uff08\u9700\u8f6c 4096 \u7ef4\u5411\u91cf\uff09<\/td>\n\u65e0\u7f1d\u517c\u5bb9\uff08768 \u7ef4\u5411\u91cf\uff09<\/td>\n<\/tr>\n
      \u5fae\u8c03\u663e\u5b58\u5360\u7528<\/td>\n25GB \/ \u5361<\/td>\n29GB \/ \u5361<\/td>\n50GB \/ \u5361\uff08\u6613\u68af\u5ea6\u7206\u70b8\uff09<\/td>\n<\/tr>\n
      \u56fd\u4ea7\u5316\u9002\u914d<\/td>\n\u652f\u6301\u5185\u7f51\u90e8\u7f72<\/td>\n\u4f9d\u8d56\u6d77\u5916\u73af\u5883<\/td>\n\u652f\u6301\u5185\u7f51\u90e8\u7f72<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      3. \u9010\u6b3e\u62c6\u89e3\uff1a\u4f18\u52bf\u3001\u52a3\u52bf\u3001\u8e29\u5751\u5b9e\u5f55<\/h4>\n

      \uff081\uff09Qwen2.5-14B\uff1a\u5de5\u4e1a\u573a\u666f \u201c\u6700\u4f18\u89e3\u201d<\/h5>\n

      \u6838\u5fc3\u4f18\u52bf\uff1a<\/p>\n

        \n
      • \u5de5\u4e1a\u672f\u8bed\u539f\u751f\u51c6\u786e\u7387 93%\uff0c\u5fae\u8c03\u540e\u8fbe 95%\uff0c\u5bf9 \u201cGSD \u6587\u4ef6\u201d\u201cProfinet \u4e22\u5305\u201d \u7b49\u5de5\u4e1a\u573a\u666f\u672f\u8bed\u7684\u7406\u89e3\u7cbe\u51c6\uff0c\u6ca1\u6709\u51fa\u73b0 \u201c\u628a GSD \u6587\u4ef6\u8bf4\u6210\u9a71\u52a8\u6587\u4ef6\u201d \u7684\u4f4e\u7ea7\u9519\u8bef\uff1b<\/li>\n
      • \u751f\u6001\u9002\u914d\u6027\u62c9\u6ee1\uff1a\u4e0e Milvus\u3001VLLM \u65e0\u7f1d\u5bf9\u63a5\uff0c\u5411\u91cf\u7ef4\u5ea6 768 \u7ef4\uff08\u548c BGE-1.5 \u4e00\u81f4\uff09\uff0c\u4e0d\u7528\u505a\u7ef4\u5ea6\u8f6c\u6362\uff0c\u68c0\u7d22\u547d\u4e2d\u7387 92%\uff1b<\/li>\n
      • \u63a8\u7406\u5ef6\u8fdf 250ms\uff0c4 \u5361 A100 \u663e\u5b58\u5360\u7528 25GB \/ \u5361\uff0c\u90e8\u7f72\u538b\u529b\u5c0f\uff1b<\/li>\n
      • \u5fae\u8c03\u7a33\u5b9a\uff1aLoRA \u9ed8\u8ba4\u53c2\u6570\uff08r=8\uff09\u5c31\u80fd\u8dd1\uff0c\u8bad\u7ec3\u65f6\u6ca1\u6709\u51fa\u73b0\u68af\u5ea6\u7206\u70b8\uff0c\u9a8c\u8bc1\u635f\u5931\u7a33\u6b65\u4e0b\u964d\u3002<\/li>\n<\/ul>\n

        \u552f\u4e00\u52a3\u52bf\uff1a<\/p>\n

          \n
        • \u5bf9\u6781\u51b7\u95e8\u7684\u5de5\u4e1a\u534f\u8bae\uff08\u5982 EtherNet\/IP V2.5\uff09\u652f\u6301\u4e00\u822c\uff0c\u539f\u751f\u51c6\u786e\u7387 85%\uff0c\u9700\u8981\u8865\u5145\u6807\u6ce8\u6570\u636e\u5fae\u8c03\u3002<\/li>\n<\/ul>\n

          \u4ee3\u7801\u793a\u4f8b\uff08Qwen2.5-14B \u521d\u59cb\u5316 + \u63a8\u7406\uff09\uff1a<\/p>\n

          from transformers import AutoModelForCausalLM, AutoTokenizer
          \nimport torch<\/p>\n

          def init_qwen_model(model_name: str = \\”qwen\/Qwen2.5-14B-Chat\\”) -> tuple:
          \n \\”\\”\\”\u521d\u59cb\u5316Qwen2.5-14B\u6a21\u578b\uff0c\u9002\u914d\u5de5\u4e1a\u573a\u666f\\”\\”\\”
          \n # \u52a0\u8f7dtokenizer
          \n tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
          \n tokenizer.pad_token = tokenizer.eos_token # \u8bbe\u7f6epad token\uff0c\u907f\u514d\u8b66\u544a
          \n # \u52a0\u8f7d\u6a21\u578b\uff08GPU\uff1a4\u5361A100\uff0c\u91cf\u5316\uff1aGPTQ 4bit\uff09
          \n model = AutoModelForCausalLM.from_pretrained(
          \n model_name,
          \n torch_dtype=torch.float16,
          \n device_map=\\”auto\\”, # \u81ea\u52a8\u5206\u914d\u8bbe\u5907
          \n trust_remote_code=True,
          \n quantization=\\”gptq\\”, # \u542f\u7528GPTQ 4bit\u91cf\u5316
          \n gptq_bits=4,
          \n gptq_group_size=32 # \u5206\u7ec4\u91cf\u5316\uff0c\u5e73\u8861\u7cbe\u5ea6\u548c\u901f\u5ea6
          \n )
          \n return model, tokenizer<\/p>\n

          def industrial_inference(model, tokenizer, query: str) -> str:
          \n \\”\\”\\”\u5de5\u4e1a\u573a\u666f\u63a8\u7406\uff1a\u8f93\u51fa\u7ed3\u6784\u5316\u89e3\u51b3\u65b9\u6848\\”\\”\\”
          \n # \u5de5\u4e1a\u573a\u666fprompt\u7ea6\u675f\uff1a\u5f3a\u5236\u7ed3\u6784\u5316\u8f93\u51fa
          \n prompt = f\\”\\”\\”
          \n \u7528\u6237\u95ee\u9898\uff1a{query}
          \n \u8981\u6c42\uff1a
          \n 1. \u5148\u89e3\u91ca\u6d89\u53ca\u7684\u5de5\u4e1a\u672f\u8bed\uff08\u5982GSD\u6587\u4ef6\u3001Profinet\u7b49\uff09\uff0c\u5fc5\u987b\u51c6\u786e\uff1b
          \n 2. \u6309\\”\u68c0\u67e5\u9879-\u64cd\u4f5c\u65b9\u6cd5-\u9a8c\u8bc1\u6807\u51c6\\”\u683c\u5f0f\u8f93\u51fa\uff0c\u64cd\u4f5c\u65b9\u6cd5\u542b\u5177\u4f53\u547d\u4ee4\u3001\u8d26\u53f7\u3001\u8def\u5f84\uff1b
          \n 3. \u907f\u514d\u6a21\u7cca\u8868\u8ff0\uff0c\u9a8c\u8bc1\u6807\u51c6\u9700\u660e\u786e\u5224\u65ad\u4f9d\u636e\uff08\u5982\u6307\u793a\u706f\u72b6\u6001\u3001\u547d\u4ee4\u7ed3\u679c\uff09\uff1b
          \n 4.<\/p>\n","protected":false},"excerpt":{"rendered":"

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