{"id":115235,"date":"2026-10-11T15:40:50","date_gmt":"2026-10-11T07:40:50","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/115235.html"},"modified":"2026-10-11T15:40:50","modified_gmt":"2026-10-11T07:40:50","slug":"%e5%a4%a7%e6%a8%a1%e5%9e%8b%e6%9c%ac%e5%9c%b0%e9%83%a8%e7%bd%b2-%e5%be%ae%e8%b0%83%e6%b5%81%e7%a8%8b%e8%af%a6%e8%a7%a3%ef%bc%8c%e9%99%84%e5%89%8d%e5%90%8e%e7%ab%af%e8%81%94%e8%b0%83%e5%ae%9e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/115235.html","title":{"rendered":"\u5927\u6a21\u578b\u672c\u5730\u90e8\u7f72 + \u5fae\u8c03\u6d41\u7a0b\u8be6\u89e3\uff0c\u9644\u524d\u540e\u7aef\u8054\u8c03\u5b9e\u6218\u6848\u4f8b"},"content":{"rendered":"

\u4e00\u3001\u5199\u5728\u524d\u9762：\u4e3a\u4ec0\u4e48\u8981\u672c\u5730\u90e8\u7f72 + \u5fae\u8c03
\n\u8c03\u7528\u4e91\u7aef API \u80fd\u5feb\u901f\u9a8c\u8bc1\u4ea7\u54c1\u60f3\u6cd5，\u4f46\u5f53\u4f60\u9762\u5bf9\u4ee5\u4e0b\u573a\u666f\u65f6，\u4e91\u7aef\u65b9\u6848\u4f1a\u78b0\u58c1：\u6570\u636e\u5408\u89c4\u8981\u6c42\u4e0d\u5141\u8bb8\u7528\u6237\u6570\u636e\u51fa\u5883\u3001\u4e1a\u52a1\u4e13\u6709\u672f\u8bed（\u5982\u4f01\u4e1a\u5185\u90e8\u4ea7\u54c1\u4ee3\u7801\u3001\u884c\u4e1a\u9ed1\u8bdd）\u4e91\u7aef\u6a21\u578b\u5b8c\u5168\u4e0d\u61c2\u3001\u9ad8\u9891\u8c03\u7528\u6210\u672c\u5728\u89c4\u6a21\u5316\u540e\u8fdc\u8d85\u81ea\u5efa\u63a8\u7406\u670d\u52a1\u3002\u672c\u5730\u90e8\u7f72\u89e3\u51b3\u201c\u6570\u636e\u5728\u54ea\u91cc\u8dd1\u201d\u7684\u95ee\u9898，\u5fae\u8c03\u89e3\u51b3\u201c\u6a21\u578b\u61c2\u4e0d\u61c2\u4f60\u7684\u4e1a\u52a1\u201d\u7684\u95ee\u9898，\u524d\u540e\u7aef\u8054\u8c03\u89e3\u51b3\u201c\u7528\u6237\u600e\u4e48\u7528\u4e0a\u5b83\u201d\u7684\u95ee\u9898\u3002\u4e09\u8005\u6784\u6210\u4e86\u4e00\u6761\u4ece\u96f6\u5230\u4e00\u7684\u5b8c\u6574\u5de5\u7a0b\u94fe\u8def\u3002<\/p>\n

\u672c\u6587\u6309\u7167\u5b9e\u9645\u5f00\u53d1\u987a\u5e8f\u7ec4\u7ec7：\u5148\u8bb2\u90e8\u7f72（\u628a\u6a21\u578b\u8dd1\u8d77\u6765），\u518d\u8bb2\u5fae\u8c03（\u8ba9\u6a21\u578b\u61c2\u4e1a\u52a1），\u6700\u540e\u7528\u5b8c\u6574\u7684\u5b9e\u6218\u6848\u4f8b\u4e32\u8054 FastAPI \u540e\u7aef\u4e0e\u524d\u7aef\u8054\u8c03\u3002<\/p>\n

\u4e8c\u3001\u672c\u5730\u90e8\u7f72：\u9009\u5bf9\u5de5\u5177，\u7b97\u6e05\u663e\u5b58
\n2.1 Ollama vs vLLM：\u4e0d\u662f\u7ade\u54c1，\u662f\u9636\u6bb5
\n\u5f88\u591a\u5f00\u53d1\u8005\u7ea0\u7ed3\u201c\u5230\u5e95\u7528 Ollama \u8fd8\u662f vLLM\u201d，\u672c\u8d28\u4e0a\u662f\u5728\u95ee\u201c\u6211\u8be5\u7528\u4ec0\u4e48\u5de5\u5177\u201d\u2014\u2014\u4f46\u8fd9\u4e24\u4e2a\u5de5\u5177\u89e3\u51b3\u7684\u662f\u4e0d\u540c\u9636\u6bb5\u7684\u95ee\u9898\u3002Ollama \u4f18\u5316\u7684\u662f\u201c\u7528\u8d77\u6765\u723d\u201d，vLLM \u4f18\u5316\u7684\u662f\u201c\u625b\u5f97\u4f4f\u538b\u201d\u3002\u5b83\u4eec\u90fd\u63d0\u4f9b\u804a\u5929\u63a5\u53e3\u3001\u90fd\u652f\u6301\u6d41\u5f0f\u8f93\u51fa，\u5355\u7528\u6237\u573a\u666f\u4e0b\u51e0\u4e4e\u611f\u89c9\u4e0d\u5230\u5dee\u522b\u2014\u2014\u5dee\u522b\u53ea\u5728\u6301\u7eed\u3001\u9ad8\u5e76\u53d1\u7684\u8d1f\u8f7d\u4e0b\u624d\u4f1a\u7206\u53d1\u3002<\/p>\n

\u67b6\u6784\u5dee\u5f02\u51b3\u5b9a\u4e86\u6027\u80fd\u5dee\u8ddd\u3002Ollama \u6536\u5230\u591a\u4e2a\u8bf7\u6c42\u65f6\u6392\u961f\u4e32\u884c\u5904\u7406\u2014\u2014\u540e\u9762\u7684\u4eba\u5fc5\u987b\u7b49\u524d\u9762\u7684\u4eba\u751f\u6210\u5b8c\u3002vLLM \u91c7\u7528\u8fde\u7eed\u6279\u5904\u7406（Continuous Batching） ：\u65b0\u8bf7\u6c42\u53ef\u4ee5\u968f\u65f6\u63d2\u8fdb\u6b63\u5728\u8ba1\u7b97\u7684\u6279\u6b21\u4e2d，GPU \u51e0\u4e4e\u6ca1\u6709\u7a7a\u95f2\u77ac\u95f4\u3002\u5b9e\u6d4b\u6570\u636e：\u5355\u7528\u6237\u65f6\u4e24\u8005\u901f\u5ea6\u51e0\u4e4e\u4e00\u6837（68 vs 64 tokens\/s），\u4f46 10 \u8def\u5e76\u53d1\u4e0b vLLM \u541e\u5410\u662f Ollama \u7684\u8fd1 4 \u500d，50 \u8def\u5e76\u53d1\u4e0b\u5dee\u8ddd\u62c9\u5230 4.5 \u500d\u3002<\/p>\n

\u663e\u5b58\u7ba1\u7406\u662f\u53e6\u4e00\u4e2a\u5206\u6c34\u5cad\u3002Ollama \u4e3a\u6bcf\u4e2a\u4f1a\u8bdd\u9884\u5148\u5206\u914d\u56fa\u5b9a\u5927\u5c0f\u7684 KV Cache\u2014\u2014\u5206\u591a\u4e86\u6d6a\u8d39\u3001\u5206\u5c11\u4e86 OOM\u3002vLLM \u7684 PagedAttention \u50cf\u64cd\u4f5c\u7cfb\u7edf\u7ba1\u7406\u865a\u62df\u5185\u5b58\u4e00\u6837，\u6309\u9875\u52a8\u6001\u5206\u914d KV Cache，\u663e\u5b58\u5229\u7528\u7387\u53ef\u4ee5\u505a\u5230 90% \u4ee5\u4e0a，\u957f\u4e0a\u4e0b\u6587\u4e5f\u4e0d\u5bb9\u6613\u7206\u663e\u5b58\u3002<\/p>\n

\u9009\u578b\u5efa\u8bae：\u4e2a\u4eba\u5f00\u53d1\u73af\u5883\u3001\u5feb\u901f\u539f\u578b\u9a8c\u8bc1\u3001\u6d88\u8d39\u7ea7 GPU \u9009 Ollama\u3002\u9762\u5411\u591a\u4eba\u7684\u751f\u4ea7\u670d\u52a1\u3001\u9700\u8981\u591a\u5361\u5206\u5e03\u5f0f\u90e8\u7f72\u3001\u8ffd\u6c42\u6781\u81f4\u541e\u5410\u9009 vLLM\u3002\u521d\u521b\u56e2\u961f\u5efa\u8bae\u4ece Ollama \u5feb\u901f\u542f\u52a8，\u6210\u719f\u670d\u52a1\u63a8\u8350\u57fa\u4e8e vLLM \u6784\u5efa\u751f\u4ea7\u7ea7\u67b6\u6784\u3002<\/p>\n

\u7ef4\u5ea6OllamavLLM
\n\u5b9a\u4f4d\u5f00\u7bb1\u5373\u7528\u7684\u672c\u5730\u6a21\u578b\u7ba1\u5bb6\u751f\u4ea7\u7ea7\u63a8\u7406\u5f15\u64ce
\n\u6279\u5904\u7406\u4e32\u884c\u6392\u961f\u8fde\u7eed\u6279\u5904\u7406
\nKV Cache\u9884\u5206\u914d\u56fa\u5b9a\u5927\u5c0fPagedAttention \u52a8\u6001\u5206\u914d
\n\u591a\u5361\u6269\u5c55\u57fa\u672c\u4e0d\u652f\u6301\u5f20\u91cf\u5e76\u884c\/\u6d41\u6c34\u7ebf\u5e76\u884c
\n\u6a21\u578b\u683c\u5f0fGGUF \u9884\u91cf\u5316HF safetensors + AWQ\/GPTQ\/FP8
\n\u9002\u7528\u573a\u666f\u4e2a\u4eba\u5f00\u53d1\u3001\u539f\u578b\u9a8c\u8bc1\u751f\u4ea7\u670d\u52a1\u3001\u9ad8\u5e76\u53d1
\n2.2 \u91cf\u5316：\u8ba9\u6d88\u8d39\u7ea7\u663e\u5361\u8dd1\u5f97\u52a8\u5927\u6a21\u578b
\n\u91cf\u5316\u7684\u672c\u8d28\u53ea\u6709\u4e00\u53e5\u8bdd：\u628a\u6bcf\u4e2a\u53c2\u6570\u7684\u5b58\u50a8\u7cbe\u5ea6\u8c03\u4f4e，\u7528\u4e00\u70b9\u7cbe\u5ea6\u6362\u4e00\u5927\u622a\u663e\u5b58\u30027B \u6a21\u578b fp16 \u6743\u91cd\u7ea6 14-15 GB，int8 \u7ea6 7-8 GB，int4 \u7ea6 4-5 GB\u30028 GB \u7684\u5361\u60f3\u8dd1 7B，fp16 \u6839\u672c\u88c5\u4e0d\u4e0b，4bit \u624d\u80fd\u8212\u9002；24 GB \u7684\u5361\u60f3\u4e0a 32B，\u4e5f\u53ea\u6709 Q4（\u7ea6 20 GB \u7ea7）\u591f\u5f97\u7740\u3002<\/p>\n

\u4e09\u6761\u91cf\u5316\u8def\u7ebf\u5404\u6709\u4e3b\u573a：<\/p>\n

GGUF（Ollama\/llama.cpp \u8def\u7ebf） ：CPU\/GPU \u901a\u7528，\u7eaf CPU \u4e5f\u80fd\u8dd1，\u95e8\u69db\u6700\u4f4e\u3002Q4_K_M \u662f\u751c\u70b9\u6863\u4f4d\u2014\u2014\u5927\u591a\u6570\u4eba\u65e5\u5e38\u95ee\u7b54\u611f\u89c9\u4e0d\u51fa\u4e0e fp16 \u7684\u5dee\u8ddd\u3002\u914d\u5408 llama.cpp \u53ef\u4ee5\u5728 CPU \u4e0e GPU \u4e4b\u95f4\u7075\u6d3b\u5206\u914d\u8ba1\u7b97\u3002<\/p>\n

AWQ（vLLM \u670d\u52a1\u9996\u9009） ：\u6fc0\u6d3b\u611f\u77e5\u91cf\u5316，\u4e0d\u4f9d\u8d56\u590d\u6742\u7684\u9010\u5c42\u91cd\u5efa\u8fc7\u7a0b，\u91cf\u5316\u901f\u5ea6\u66f4\u5feb\u3002AWQ \u91cf\u5316\u540e\u6a21\u578b\u4f53\u79ef\u53ef\u7f29\u51cf 75%，\u63a8\u7406\u901f\u5ea6\u63d0\u5347 20-40%，\u7528 AutoGPTQ \u91cf\u5316\u7684\u6743\u91cd\u53ef\u76f4\u63a5\u88ab vLLM \u52a0\u8f7d\u3002<\/p>\n

GPTQ（PyTorch \u751f\u6001\u517c\u5bb9） ：\u5728 GPU \u6d88\u8d39\u7ea7\u786c\u4ef6\u4e0a\u8868\u73b0\u4f18\u79c0，\u5ef6\u8fdf\u964d\u4f4e\u663e\u8457\u3002GPTQ-Int4 \u91cf\u5316\u540e\u6a21\u578b\u540c\u6837\u7f29\u51cf 75%，\u9002\u5408\u9700\u8981\u4e0e transformers \u751f\u6001\u65e0\u7f1d\u8854\u63a5\u7684\u573a\u666f\u3002<\/p>\n

\u91cf\u5316\u4e0d\u662f\u514d\u8d39\u7684\u3002\u4f4d\u5bbd\u8d8a\u4f4e，\u4e22\u7684\u4fe1\u606f\u8d8a\u591a\u2014\u2014Q8 \u63a5\u8fd1\u65e0\u635f，Q4 \u662f\u201c\u751c\u70b9\u201d，Q2\/Q3 \u5219\u4f1a\u660e\u663e\u6389\u667a\u5546（\u8f93\u51fa\u91cd\u590d\u3001\u903b\u8f91\u65ad\u88c2）\u3002\u5b9e\u9645\u8dd1\u8d77\u6765\u8fd8\u4f1a\u53d1\u73b0：\u91cf\u5316\u7701\u7684\u4e0d\u53ea\u662f\u6743\u91cd\u663e\u5b58，KV Cache \u4e5f\u4f1a\u88ab\u4e00\u8d77\u538b\u7f29\u2014\u2014Q4 \u7684 7B \u5728 8 GB \u5361\u4e0a\u5f00 4K \u4e0a\u4e0b\u6587\u8fd8\u80fd\u7559\u51fa\u5e76\u53d1\u4f59\u91cf，\u800c fp16 \u8fde\u52a0\u8f7d\u90fd\u505a\u4e0d\u5230\u3002<\/p>\n

2.3 \u786c\u4ef6\u9009\u578b\u901f\u67e5\u8868
\n\u4ee5 Qwen3.8-27B \u4e3a\u53c2\u8003\u6837\u672c（\u5b98\u65b9 BF16 \u6743\u91cd\u5b9e\u6d4b 51.75 GB，FP8 \u6743\u91cd 28.75 GB），\u4e0d\u540c\u786c\u4ef6\u7684\u9002\u914d\u6863\u4f4d\u5982\u4e0b：<\/p>\n

\u663e\u5b58\u6863\u4f4d\u4ee3\u8868\u663e\u5361\u53ef\u8dd1\u6a21\u578b\u63a8\u8350\u91cf\u5316\u6863\u4f4d\u5178\u578b\u901f\u5ea6
\n8-12 GBRTX 4060 8G \/ 5070 12G7B-9BQ4_K_M \/ AWQ INT425-40 tok\/s
\n16 GBRTX 4060 Ti 16G \/ 5060 Ti 16G14B-27BQ4_K_M \/ FP830-50 tok\/s
\n24 GBRTX 4090 \/ 5090 32G27B-35BQ4_K_M \/ AWQ INT440-80 tok\/s
\n48 GB+A6000 \/ A100 40G70BAWQ INT4 \/ GPTQ INT415-25 tok\/s
\n\u5bf9\u4e8e MoE \u67b6\u6784\u6a21\u578b，Strata \u5f15\u64ce\u7b49\u65b9\u6848\u901a\u8fc7\u5c06 MoE \u6574\u4f53\u8f7d\u5165 RAM\u3001\u4ec5\u5c06\u9ad8\u9891\u4e13\u5bb6\u8f7d\u5165 VRAM，\u4f7f 12GB \u663e\u5b58\u7684 RTX 5070 \u4e5f\u80fd\u8fd0\u884c 125B \u53c2\u6570\u7684 Qwen3.8-Flash-Next \u6a21\u578b，2-bit \u91cf\u5316\u7248\u8fbe\u5230 94 \u8bcd\u5143\/\u79d2\u7684\u63a8\u7406\u901f\u5ea6\u3002<\/p>\n

\u4e09\u3001\u5fae\u8c03：\u4ece\u201c\u901a\u7528\u5b66\u9738\u201d\u5230\u201c\u9886\u57df\u4e13\u5bb6\u201d
\n3.1 \u5fae\u8c03\u65b9\u6cd5\u9009\u578b
\n\u73b0\u6210\u7684\u5927\u6a21\u578b\u5c31\u50cf\u535a\u5b66\u7684\u201c\u901a\u624d\u5b66\u9738\u201d，\u5b83\u77e5\u9053\u5f88\u591a\u901a\u7528\u77e5\u8bc6，\u4f46\u5bf9\u4f60\u516c\u53f8\u7279\u6709\u7684\u4ea7\u54c1\u4ee3\u7801\u3001\u884c\u4e1a\u5185\u90e8\u7684\u62a5\u544a\u683c\u5f0f\u5374\u4e00\u65e0\u6240\u77e5\u3002\u5fae\u8c03\u5c31\u662f\u8ba9\u901a\u7528\u6a21\u578b\u5feb\u901f\u5b66\u4f1a\u4f60\u7684\u201c\u72ec\u95e8\u79d8\u7c4d\u201d\u3002<\/p>\n

LoRA \u662f\u76ee\u524d\u6700\u6d41\u884c\u3001\u6700\u5b9e\u7528\u7684\u5fae\u8c03\u65b9\u6cd5\u3002\u5b83\u4e0d\u7ed9\u6a21\u578b\u5927\u8111\u52a8\u624b\u672f，\u800c\u662f\u5728\u65c1\u8fb9\u52a0\u4e00\u4e2a\u201c\u5916\u6302\u77e5\u8bc6\u6a21\u5757\u201d\u2014\u2014\u51bb\u7ed3\u9884\u8bad\u7ec3\u6743\u91cd，\u5728 Transformer \u5c42\u7684\u6ce8\u610f\u529b\u6a21\u5757\u65c1\u8def\u6dfb\u52a0\u53ef\u8bad\u7ec3\u7684\u4f4e\u79e9\u5206\u89e3\u77e9\u9635\u3002\u5047\u8bbe\u539f\u59cb\u6743\u91cd\u4e3a W，LoRA \u5f15\u5165 W\u2018 = W + BA，\u5176\u4e2d B \u548c A \u662f\u4f4e\u79e9\u77e9\u9635，\u8bad\u7ec3\u65f6\u53ea\u66f4\u65b0\u8fd9\u4e24\u4e2a\u5c0f\u77e9\u9635\u3002\u79e9 r \u901a\u5e38\u9009 4-64，\u63a8\u8350\u4ece 8 \u6216 16 \u5f00\u59cb\u5c1d\u8bd5\u3002<\/p>\n

QLoRA \u5728 LoRA \u57fa\u7840\u4e0a\u5f15\u5165 4-bit \u91cf\u5316\u7684\u57fa\u5ea7\u6a21\u578b，\u4f7f\u7528 NF4 \u6570\u636e\u7c7b\u578b\u548c\u53cc\u91cd\u91cf\u5316\u6280\u672f，\u8fdb\u4e00\u6b65\u5c06\u663e\u5b58\u9700\u6c42\u964d\u4f4e\u81f3\u539f\u6709 LoRA \u7684 1\/3 \u5de6\u53f3\u3002\u4f8b\u5982，\u7528 QLoRA \u5fae\u8c03 70B \u6a21\u578b\u4ec5\u9700 48GB \u663e\u5b58\u3002<\/p>\n

3.2 \u6570\u636e\u51c6\u5907：80% \u7684\u7cbe\u529b\u5728\u8fd9\u91cc
\n\u5fae\u8c03\u7684\u6548\u679c\u4e0a\u9650\u7531\u6570\u636e\u8d28\u91cf\u51b3\u5b9a，\u800c\u975e\u8bad\u7ec3\u6280\u5de7\u3002LLaMA-Factory \u9ed8\u8ba4\u652f\u6301\u6700\u901a\u7528\u7684 Alpaca \u683c\u5f0f（\u4e00\u4e2a\u5305\u542b\u201c\u6307\u4ee4-\u8f93\u5165-\u8f93\u51fa\u201d\u5bf9\u7684 JSON \u5217\u8868\u6587\u4ef6）：<\/p>\n

json
\n[
\n{
\n\u201cinstruction\u201d: \u201c\u8bf7\u628a\u4ee5\u4e0b\u60a3\u8005\u7684\u4fd7\u8bed\u7ffb\u8bd1\u4e3a\u4e13\u4e1a\u533b\u5b66\u672f\u8bed\u3002\u201d,
\n\u201cinput\u201d: \u201c\u6211\u55d3\u5b50\u6709\u70b9\u5e72，\u8eab\u4e0a\u89c9\u5f97\u53d1\u70eb\u3002\u201d,
\n\u201coutput\u201d: \u201c\u60a3\u8005\u81ea\u8bc9\u8f7b\u5ea6\u54bd\u90e8\u5145\u8840，\u4f34\u968f\u4f4e\u70ed\u75c7\u72b6\u3002\u201d
\n},
\n{
\n\u201cinstruction\u201d: \u201c\u8bf7\u628a\u4ee5\u4e0b\u60a3\u8005\u7684\u4fd7\u8bed\u7ffb\u8bd1\u4e3a\u4e13\u4e1a\u533b\u672f\u8bed\u3002\u201d,
\n\u201cinput\u201d: \u201c\u6211\u809a\u5b50\u75bc\u5f97\u62e7\u6210\u4e86\u9ebb\u82b1\u3002\u201d,
\n\u201coutput\u201d: \u201c\u60a3\u8005\u81ea\u8bc9\u4f34\u6709\u6025\u6027\u8179\u90e8\u75c9\u631b\u6027\u75bc\u75db\u3002\u201d
\n}
\n]
\n\u6307\u4ee4（instruction） \u662f\u4f60\u5e0c\u671b\u7528\u6237\u5bf9 AI \u53d1\u9001\u7684\u547d\u4ee4，\u8f93\u5165（input） \u662f\u989d\u5916\u7684\u4e0a\u4e0b\u6587\u80cc\u666f，\u8f93\u51fa（output） \u662f\u4f60\u5e0c\u671b AI \u6a21\u4eff\u7684\u6807\u51c6\u7b54\u6848\u3002\u6570\u636e\u6765\u6e90\u53ef\u4ee5\u4ece\u4e13\u4e1a\u4e66\u7c4d\u3001\u8bba\u6587\u3001\u5185\u90e8\u6587\u6863\u4e2d\u62bd\u53d6，\u5e76\u5229\u7528\u5927\u6a21\u578b\u8fdb\u884c\u77e5\u8bc6\u84b8\u998f\u548c\u601d\u7ef4\u94fe\u589e\u5f3a，\u6700\u540e\u52a1\u5fc5\u8bf7\u9886\u57df\u4e13\u5bb6\u5ba1\u6838\u3002<\/p>\n

\u6570\u636e\u8d28\u91cf\u7684\u4e09\u4e2a\u5173\u952e\u539f\u5219：\u591a\u6837\u6027\u2014\u2014\u8986\u76d6\u4e1a\u52a1\u573a\u666f\u4e2d\u7684\u5404\u79cd\u8868\u8fbe\u65b9\u5f0f，\u907f\u514d\u6a21\u578b\u53ea\u5b66\u4f1a\u4e00\u79cd\u95ee\u6cd5；\u4e00\u81f4\u6027\u2014\u2014\u540c\u4e00\u7c7b\u95ee\u9898\u7684\u56de\u7b54\u98ce\u683c\u548c\u683c\u5f0f\u8981\u4fdd\u6301\u7edf\u4e00；\u89c4\u6a21\u9002\u4e2d\u2014\u2014\u901a\u5e38 500-2000 \u6761\u9ad8\u8d28\u91cf\u6837\u672c\u5373\u53ef\u8ba9\u6a21\u578b\u5b66\u4f1a\u4e00\u4e2a\u7279\u5b9a\u4efb\u52a1，\u5173\u952e\u662f\u8d28\u91cf\u800c\u975e\u6570\u91cf\u3002<\/p>\n

3.3 LLaMA-Factory \u5fae\u8c03\u5b9e\u6218
\nLLaMA-Factory \u662f\u4e00\u4e2a\u4e00\u7ad9\u5f0f\u7684\u6a21\u578b\u8bad\u7ec3\u63a7\u5236\u53f0，\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7f51\u9875\u53ef\u89c6\u5316\u754c\u9762，\u8ba9\u4f60\u53ea\u9700\u901a\u8fc7\u9f20\u6807\u201c\u70b9\u9009\u201d\u5c31\u80fd\u5b8c\u6210\u5927\u6a21\u578b\u7684\u5168\u90e8\u8bad\u7ec3\u914d\u7f6e\u4e0e\u542f\u52a8\u3002<\/p>\n

\u73af\u5883\u642d\u5efa：<\/p>\n

bash
\ngit clone https:\/\/github.com\/hiyouga\/LLaMA-Factory.git
\ncd LLaMA-Factory
\npip install -e .[metrics,bitsandbytes]
\n\u542f\u52a8 Web UI：<\/p>\n

bash
\nllamafactory-cli webui
\n\u8bbf\u95ee http:\/\/localhost:7860 \u540e，\u5728\u9762\u677f\u4e2d\u914d\u7f6e\u4ee5\u4e0b\u6838\u5fc3\u53c2\u6570：<\/p>\n

\u53c2\u6570\u63a8\u8350\u503c\u8bf4\u660e
\n\u5fae\u8c03\u65b9\u6cd5LoRA\u4ec5\u8bad\u7ec3\u7ea6 0.1% \u7684\u53c2\u6570，\u663e\u5b58\u5360\u7528\u964d\u4f4e 80%
\n\u5fae\u8c03\u9636\u6bb5Supervised Fine-Tuning (SFT)\u76d1\u7763\u5fae\u8c03，\u6700\u5e38\u7528\u7684\u5fae\u8c03\u5f62\u5f0f
\n\u5b66\u4e60\u73872e-4LoRA \u6807\u51c6\u5b66\u4e60\u7387
\n\u8bad\u7ec3\u8f6e\u65703\u8ba9\u6a21\u578b\u628a\u6570\u636e\u96c6\u4ece\u5934\u5230\u5c3e\u5b66\u4e60 3 \u904d
\nLoRA \u79e9 \u00ae8 \u6216 16\u79e9\u8d8a\u5927\u8868\u8fbe\u80fd\u529b\u8d8a\u5f3a，\u4f46\u8fc7\u62df\u5408\u98ce\u9669\u4e5f\u589e\u52a0
\nLoRA Alpha16 \u6216 32\u884c\u4e1a\u901a\u7528\u6700\u4f18\u53c2\u6570\u7ec4\u5408，\u517c\u987e\u62df\u5408\u4e0e\u9632\u8fc7\u62df\u5408
\n\u6279\u6b21\u5927\u5c0f2-4\u89c6\u663e\u5b58\u800c\u5b9a，24GB \u663e\u5b58\u5efa\u8bae 4
\n\u8bad\u7ec3\u5b8c\u6210\u540e，LoRA \u9002\u914d\u5668\u6743\u91cd\u6587\u4ef6\u901a\u5e38\u53ea\u6709 10-50 MB\u2014\u2014\u8fd9\u662f LoRA \u6700\u5927\u7684\u5de5\u7a0b\u4f18\u52bf：\u53ef\u63d2\u62d4，\u4e0d\u540c\u4efb\u52a1\u5207\u6362\u4e0d\u540c LoRA \u6743\u91cd，\u65e0\u9700\u91cd\u65b0\u52a0\u8f7d\u57fa\u5ea7\u6a21\u578b\u3002<\/p>\n

3.4 \u6a21\u578b\u5408\u5e76\u4e0e\u5bfc\u51fa
\n\u8bad\u7ec3\u4ea7\u51fa\u7684\u662f LoRA \u9002\u914d\u5668，\u90e8\u7f72\u65f6\u9700\u8981\u5c06\u5176\u4e0e\u57fa\u5ea7\u6a21\u578b\u5408\u5e76，\u6216\u8005\u8ba9\u63a8\u7406\u6846\u67b6\u540c\u65f6\u52a0\u8f7d\u57fa\u5ea7\u6a21\u578b\u548c\u9002\u914d\u5668\u3002<\/p>\n

\u65b9\u5f0f\u4e00：\u5408\u5e76\u5bfc\u51fa（\u9002\u5408 Ollama \u90e8\u7f72） ：<\/p>\n

python
\nfrom peft import PeftModel
\nfrom transformers import AutoModelForCausalLM, AutoTokenizer<\/p>\n

base_model = AutoModelForCausalLM.from_pretrained(\u201cQwen\/Qwen3-0.6B\u201d)
\nlora_model = PeftModel.from_pretrained(base_model, \u201cpath\/to\/lora_output\u201d)
\nmerged_model = lora_model.merge_and_unload()
\nmerged_model.save_pretrained(\u201cpath\/to\/merged_model\u201d)
\ntokenizer.save_pretrained(\u201cpath\/to\/merged_model\u201d)
\n\u5408\u5e76\u540e\u7684\u6a21\u578b\u4e0e\u539f\u59cb\u6a21\u578b\u5b8c\u5168\u517c\u5bb9，\u53ef\u76f4\u63a5\u7528 transformers \u6807\u51c6\u6d41\u7a0b\u90e8\u7f72\u3002\u6ce8\u610f：\u4ec5\u5728\u63a8\u7406\u524d\u5408\u5e76，\u8bad\u7ec3\u4e2d\u5e94\u4fdd\u6301\u5206\u79bb\u4ee5\u4fbf\u591a\u4efb\u52a1\u5207\u6362\u3002<\/p>\n

\u65b9\u5f0f\u4e8c：\u52a8\u6001\u52a0\u8f7d\u9002\u914d\u5668（\u9002\u5408 vLLM \u90e8\u7f72） ：vLLM \u652f\u6301 Multi-LoRA \u6258\u7ba1\u80fd\u529b，\u5141\u8bb8\u5728\u540c\u4e00\u63a8\u7406\u5b9e\u4f8b\u4e0a\u52a8\u6001\u52a0\u8f7d\u591a\u4e2a LoRA \u9002\u914d\u5668，\u5bf9\u4e8e\u9700\u8981\u670d\u52a1\u591a\u4e2a\u5b9a\u5236\u5316\u6a21\u578b\u7684\u573a\u666f\u6781\u5177\u4ef7\u503c\u3002<\/p>\n

\u56db\u3001\u524d\u540e\u7aef\u8054\u8c03\u5b9e\u6218：\u4ece\u6743\u91cd\u6587\u4ef6\u5230\u53ef\u7528\u4ea7\u54c1
\n4.1 \u6574\u4f53\u67b6\u6784
\n\u8bad\u7ec3\u5b8c\u6a21\u578b\u53ea\u662f\u7b2c\u4e00\u6b65\u3002\u6a21\u578b\u4e0d\u63d0\u4f9b\u670d\u52a1，\u5c31\u53ea\u662f\u4e00\u5806\u8eba\u5728\u78c1\u76d8\u4e0a\u7684\u6743\u91cd\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u4ece\u8bad\u7ec3\u5230\u4e0a\u7ebf\u7684\u67b6\u6784：<\/p>\n

text
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u524d\u7aef（React \/ Vue \/ \u6d4f\u89c8\u5668） \u2502
\n\u2502 EventSource \/ fetch + ReadableStream \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n\u2502 HTTP \/ SSE
\n\u25bc
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 FastAPI \u63a8\u7406\u670d\u52a1（uvicorn） \u2502
\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n\u2502 \u2502 GET \/health \u2192 \u5065\u5eb7\u68c0\u67e5 \u2502 \u2502
\n\u2502 \u2502 POST \/chat \u2192 \u5355\u6761\u5bf9\u8bdd（\u6d41\u5f0f\/\u975e\u6d41\u5f0f） \u2502 \u2502
\n\u2502 \u2502 POST \/chat\/batch \u2192 \u6279\u91cf\u5bf9\u8bdd \u2502 \u2502
\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n\u2502 \u2502 Tokenizer (chat_template) \u2502 \u2502
\n\u2502 \u2502 PeftModel (Base + LoRA adapter) \u2502 \u2502
\n\u2502 \u2502 model.generate() \u2192 reply \u2502 \u2502
\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n4.2 FastAPI \u540e\u7aef\u670d\u52a1
\n\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684 FastAPI \u63a8\u7406\u670d\u52a1\u4ee3\u7801，\u652f\u6301\u5355\u6761\u5bf9\u8bdd\u3001\u6279\u91cf\u5bf9\u8bdd\u3001\u5065\u5eb7\u68c0\u67e5\u548c SSE \u6d41\u5f0f\u8f93\u51fa：<\/p>\n

python
\n\u201c\u201d"
\nQwen3-0.6B + LoRA FastAPI \u63a8\u7406\u670d\u52a1
\n\u542f\u52a8：uvicorn fastapi_server:app –host 0.0.0.0 –port 8000
\n\u201c\u201d"
\nimport json
\nimport torch
\nfrom fastapi import FastAPI, HTTPException
\nfrom fastapi.responses import StreamingResponse
\nfrom pydantic import BaseModel
\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
\nfrom peft import PeftModel
\nfrom threading import Thread<\/p>\n

==================== \u914d\u7f6e\u533a ====================<\/h2>\n

MODEL_PATH = \u201c.\/Qwen3-0.6B\u201d
\nLORA_PATH = \u201c.\/qwen_lora_output\u201d<\/p>\n

=================================================<\/h2>\n

app = FastAPI(title=\u201cLoRA Inference Service\u201d)<\/p>\n

\u542f\u52a8\u65f6\u52a0\u8f7d\u6a21\u578b（\u53ea\u52a0\u8f7d\u4e00\u6b21，\u5e38\u9a7b\u5185\u5b58）<\/h2>\n

print(\u201c\u23f3 \u6b63\u5728\u52a0\u8f7d Tokenizer \u2026\u201d)
\ntok = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
\nprint(\u201c\u23f3 \u6b63\u5728\u52a0\u8f7d Base Model \u2026\u201d)
\nbase = AutoModelForCausalLM.from_pretrained(
\nMODEL_PATH, torch_dtype=torch.float16, device_map=\u201cauto\u201d, trust_remote_code=True
\n)
\nprint(\u201c\u23f3 \u6b63\u5728\u52a0\u8f7d LoRA Adapter \u2026\u201d)
\nmodel = PeftModel.from_pretrained(base, LORA_PATH)
\nmodel.eval()
\nprint(\u201c\u2705 \u6a21\u578b\u52a0\u8f7d\u5b8c\u6210\u201d)<\/p>\n

class ChatRequest(BaseModel):
\nmessage: str
\nmax_tokens: int = 512
\ntemperature: float = 0.7<\/p>\n

@app.get(\u201c\/health\u201d)
\nasync def health():
\nreturn {\u201cstatus\u201d: \u201cok\u201d, \u201cmodel\u201d: \u201cQwen3-0.6B+LoRA\u201d}<\/p>\n

@app.post(\u201c\/chat\u201d)
\nasync def chat(req: ChatRequest):
\nmessages = [{\u201crole\u201d: \u201cuser\u201d, \u201ccontent\u201d: req.message}]
\ntext = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
\ninputs = tok(text, return_tensors=\u201cpt\u201d).to(model.device)
\nwith torch.no_grad():
\noutputs = model.generate(
\n**inputs, max_new_tokens=req.max_tokens,
\ntemperature=req.temperature, do_sample=True,
\n)
\nreply = tok.decode(outputs[0][inputs[\u201cinput_ids\u201d].shape[1]:], skip_special_tokens=True)
\nreturn {\u201creply\u201d: reply}<\/p>\n

@app.post(\u201c\/chat\/stream\u201d)
\nasync def chat_stream(req: ChatRequest):
\nmessages = [{\u201crole\u201d: \u201cuser\u201d, \u201ccontent\u201d: req.message}]
\ntext = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
\ninputs = tok(text, return_tensors=\u201cpt\u201d).to(model.device)
\nstreamer = TextIteratorStreamer(tok, skip_prompt=True, skip_special_tokens=True)<\/p>\n

generation_kwargs = dict(
\n **inputs, max_new_tokens=req.max_tokens,
\n temperature=req.temperature, do_sample=True, streamer=streamer,
\n)
\nthread = Thread(target=model.generate, kwargs=generation_kwargs)
\nthread.start()<\/p>\n

async def event_generator():
\n for token in streamer:
\n yield f"data: {json.dumps({'token': token})}\\\\n\\\\n"
\n yield "data: [DONE]\\\\n\\\\n"<\/p>\n

return StreamingResponse(event_generator(), media_type="text\/event-stream")<\/p>\n

\u5173\u952e\u8bbe\u8ba1\u51b3\u7b56：\u6a21\u578b\u5728\u542f\u52a8\u65f6\u4e00\u6b21\u6027\u52a0\u8f7d\u5e76\u5e38\u9a7b\u5185\u5b58，\u907f\u514d\u6bcf\u6b21\u8bf7\u6c42\u91cd\u65b0\u52a0\u8f7d\u5e26\u6765\u7684\u6570\u79d2\u5ef6\u8fdf\u3002\/chat\/stream \u7aef\u70b9\u4f7f\u7528 TextIteratorStreamer \u5c06\u6a21\u578b\u751f\u6210\u8fc7\u7a0b\u5f02\u6b65\u5316\u2014\u2014\u6a21\u578b\u5728\u540e\u53f0\u7ebf\u7a0b\u4e2d\u751f\u6210，\u4e3b\u7ebf\u7a0b\u901a\u8fc7 SSE \u5c06 token \u9010\u4e2a\u63a8\u9001\u7ed9\u524d\u7aef\u3002<\/p>\n

4.3 \u524d\u7aef\u8054\u8c03
\n\u524d\u7aef\u901a\u8fc7 fetch + ReadableStream \u6d88\u8d39 SSE \u6d41（\u76f8\u6bd4 EventSource \u66f4\u7075\u6d3b，\u652f\u6301 POST \u8bf7\u6c42）：<\/p>\n

javascript
\nasync function streamChat(message) {
\nconst response = await fetch(\u201chttp:\/\/localhost:8000\/chat\/stream\u201d, {
\nmethod: \u201cPOST\u201d,
\nheaders: { \u201cContent-Type\u201d: \u201capplication\/json\u201d },
\nbody: JSON.stringify({ message, max_tokens: 512 }),
\n});<\/p>\n

const reader = response.body.getReader();
\nconst decoder = new TextDecoder();
\nlet buffer = \u201c\u201d;<\/p>\n

while (true) {
\nconst { done, value } = await reader.read();
\nif (done) break;
\nbuffer += decoder.decode(value, { stream: true });<\/p>\n

const lines = buffer.split("\\\\n\\\\n");
\nbuffer = lines.pop();<\/p>\n

for (const line of lines) {
\n if (!line.startsWith("data: ")) continue;
\n const payload = line.slice(6);
\n if (payload === "[DONE]") return;
\n const data = JSON.parse(payload);
\n process.stdout.write(data.token);
\n}<\/p>\n

}
\n}
\n\u8fd9\u79cd\u6d41\u5f0f\u8f93\u51fa\u7684\u611f\u77e5\u4f53\u9a8c\u8fdc\u4f18\u4e8e\u7b49\u5f85\u5b8c\u6574\u54cd\u5e94\u2014\u2014\u9996 token \u5728\u7ea6 200ms \u5185\u51fa\u73b0，\u7528\u6237\u770b\u5230\u7684\u662f\u9010\u5b57\u8f93\u51fa\u7684\u5b9e\u65f6\u6548\u679c，\u800c\u975e\u6570\u79d2\u767d\u5c4f\u3002<\/p>\n

4.4 \u751f\u4ea7\u90e8\u7f72\u6ce8\u610f\u4e8b\u9879
\n\u9274\u6743：\u5728 FastAPI \u4e2d\u6dfb\u52a0 API Key \u9a8c\u8bc1\u4e2d\u95f4\u4ef6，\u6216\u4f7f\u7528 Nginx \u53cd\u5411\u4ee3\u7406\u5c42\u5b9e\u73b0\u9274\u6743\u3002<\/p>\n

\u53cd\u5411\u4ee3\u7406：Nginx \u9700\u8981\u914d\u7f6e proxy_buffering off \u4ee5\u652f\u6301 SSE \u6d41\u5f0f\u4f20\u8f93\u3002<\/p>\n

\u5e76\u53d1\u63a7\u5236：\u5355\u5361\u90e8\u7f72\u65f6，\u8bbe\u7f6e\u6700\u5927\u5e76\u53d1\u6570\u907f\u514d\u663e\u5b58\u6ea2\u51fa\u3002\u53ef\u4ee5\u5728 FastAPI \u5c42\u4f7f\u7528 asyncio.Semaphore \u9650\u5236\u540c\u65f6\u8fdb\u884c\u7684\u751f\u6210\u8bf7\u6c42\u6570\u91cf\u3002<\/p>\n

\u76d1\u63a7：\u8bb0\u5f55\u6bcf\u6b21\u8bf7\u6c42\u7684 token \u6d88\u8017\u3001\u5ef6\u8fdf\u3001\u6a21\u578b\u7248\u672c，\u4fbf\u4e8e\u540e\u7eed\u5206\u6790\u548c\u5bb9\u91cf\u89c4\u5212\u3002<\/p>\n

\u4e94\u3001\u603b\u7ed3
\n\u4ece\u96f6\u642d\u5efa\u4e00\u4e2a\u5b8c\u6574\u7684\u5927\u6a21\u578b\u5e94\u7528，\u6838\u5fc3\u6d41\u7a0b\u53ef\u4ee5\u603b\u7ed3\u4e3a\u4e94\u4e2a\u6b65\u9aa4：<\/p>\n

\u7b2c\u4e00\u6b65：\u9009\u90e8\u7f72\u5de5\u5177\u3002 \u4e2a\u4eba\u5f00\u53d1\u7528 Ollama \u5feb\u901f\u9a8c\u8bc1，\u751f\u4ea7\u670d\u52a1\u7528 vLLM \u625b\u5e76\u53d1\u3002<\/p>\n

\u7b2c\u4e8c\u6b65：\u5b9a\u91cf\u5316\u6863\u4f4d\u3002 \u6839\u636e\u663e\u5361\u663e\u5b58\u9009\u62e9\u91cf\u5316\u65b9\u6848\u2014\u20148GB \u9009 Q4_K_M，16GB \u9009 Q4_K_M \u6216 FP8，24GB \u9009 AWQ INT4\u3002<\/p>\n

\u7b2c\u4e09\u6b65：\u51c6\u5907\u5fae\u8c03\u6570\u636e\u3002 80% \u7684\u7cbe\u529b\u82b1\u5728\u6570\u636e\u4e0a，500-2000 \u6761\u9ad8\u8d28\u91cf Alpaca \u683c\u5f0f\u6837\u672c\u5373\u53ef\u8ba9\u6a21\u578b\u5b66\u4f1a\u4e00\u4e2a\u7279\u5b9a\u4efb\u52a1\u3002<\/p>\n

\u7b2c\u56db\u6b65：LLaMA-Factory \u5fae\u8c03\u3002 LoRA \u79e9 8-16，\u5b66\u4e60\u7387 2e-4，\u8bad\u7ec3 3 \u8f6e，\u4ea7\u51fa 10-50MB \u7684\u9002\u914d\u5668\u6587\u4ef6\u3002<\/p>\n

\u7b2c\u4e94\u6b65：FastAPI \u670d\u52a1\u5316 + \u524d\u7aef\u8054\u8c03\u3002 \u6a21\u578b\u5e38\u9a7b\u5185\u5b58，SSE \u6d41\u5f0f\u8f93\u51fa，\u524d\u540e\u7aef\u901a\u8fc7 HTTP + SSE \u5b8c\u6210\u5b9e\u65f6\u4ea4\u4e92\u3002<\/p>\n

2026 \u5e74\u7684\u5927\u6a21\u578b\u672c\u5730\u90e8\u7f72\u5df2\u7ecf\u4ece\u201c\u53ea\u6709\u5927\u5382\u73a9\u5f97\u8d77\u201d\u8fdb\u5165\u201c\u6d88\u8d39\u7ea7\u663e\u5361\u4e5f\u80fd\u8dd1\u201d\u7684\u9636\u6bb5\u3002\u771f\u6b63\u62c9\u5f00\u5dee\u8ddd\u7684\u4e0d\u662f\u6a21\u578b\u9009\u578b，\u800c\u662f\u5de5\u7a0b\u7ec6\u8282\u2014\u2014\u91cf\u5316\u6863\u4f4d\u662f\u5426\u7b97\u5bf9\u3001\u6570\u636e\u8d28\u91cf\u662f\u5426\u8fbe\u6807\u3001\u6d41\u5f0f\u8f93\u51fa\u662f\u5426\u987a\u7545\u3001\u5e76\u53d1\u63a7\u5236\u662f\u5426\u5230\u4f4d\u3002\u628a\u8fd9\u4e94\u4e2a\u6b65\u9aa4\u7684\u6bcf\u4e00\u4e2a\u7ec6\u8282\u505a\u5230\u4f4d，\u624d\u662f\u771f\u6b63\u7684\u7ade\u4e89\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u4e00\u3001\u5199\u5728\u524d\u9762：\u4e3a\u4ec0\u4e48\u8981\u672c\u5730\u90e8\u7f72 \u5fae\u8c03
\n\u8c03\u7528\u4e91\u7aef API \u80fd\u5feb\u901f\u9a8c\u8bc1\u4ea7\u54c1\u60f3\u6cd5，\u4f46\u5f53\u4f60\u9762\u5bf9\u4ee5\u4e0b\u573a\u666f\u65f6，\u4e91\u7aef\u65b9\u6848\u4f1a\u78b0\u58c1：\u6570\u636e\u5408\u89c4\u8981\u6c42\u4e0d\u5141\u8bb8\u7528\u6237\u6570\u636e\u51fa\u5883\u3001\u4e1a\u52a1\u4e13\u6709\u672f\u8bed（\u5982\u4f01\u4e1a\u5185\u90e8\u4ea7\u54c1\u4ee3\u7801\u3001\u884c\u4e1a\u9ed1\u8bdd）\u4e91\u7aef\u6a21\u578b\u5b8c\u5168\u4e0d\u61c2\u3001\u9ad8\u9891\u8c03\u7528\u6210\u672c\u5728\u89c4\u6a21\u5316\u540e\u8fdc\u8d85\u81ea\u5efa\u63a8\u7406\u670d\u52a1\u3002\u672c\u5730\u90e8\u7f72\u89e3\u51b3\u201c\u6570\u636e\u5728\u54ea\u91cc\u8dd1\u201d\u7684\u95ee\u9898，\u5fae\u8c03\u89e3\u51b3\u201c\u6a21\u578b\u61c2\u4e0d\u61c2\u4f60\u7684\u4e1a\u52a1\u201d\u7684\u95ee\u9898，\u524d\u540e\u7aef\u8054\u8c03\u89e3\u51b3\u201c\u7528\u6237\u600e\u4e48\u7528\u4e0a\u5b83\u201d\u7684\u95ee\u9898\u3002\u4e09\u8005<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[53],"topic":[],"class_list":["post-115235","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai"],"yoast_head":"\n\u5927\u6a21\u578b\u672c\u5730\u90e8\u7f72 + \u5fae\u8c03\u6d41\u7a0b\u8be6\u89e3\uff0c\u9644\u524d\u540e\u7aef\u8054\u8c03\u5b9e\u6218\u6848\u4f8b - 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