{"id":68776,"date":"2026-01-30T18:09:13","date_gmt":"2026-01-30T10:09:13","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/68776.html"},"modified":"2026-01-30T18:09:13","modified_gmt":"2026-01-30T10:09:13","slug":"%e3%80%90%e4%bf%a1%e5%88%9b%e3%80%91%e5%8d%8e%e4%b8%ba%e6%98%87%e8%85%be%e5%a4%a7%e6%a8%a1%e5%9e%8b%e9%83%a8%e7%bd%b2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/68776.html","title":{"rendered":"\u3010\u4fe1\u521b\u3011\u534e\u4e3a\u6607\u817e\u5927\u6a21\u578b\u90e8\u7f72"},"content":{"rendered":"

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

\u4e00\u3001\u7cfb\u7edf\u4e0e\u786c\u4ef6\u73af\u5883\u8bf4\u660e<\/h3>\n\n\u6a21\u5757\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n\n
CPU<\/td>\n\u9cb2\u9e4f 920 \/ 930 \u7cfb\u5217（ARM64\u67b6\u6784）<\/td>\n<\/tr>\n
GPU\/NPU<\/td>\n\u534e\u4e3a\u6607\u817e 910B2 \u00d7 2（\u652f\u6301BF16\u3001INT8\u91cf\u5316）<\/td>\n<\/tr>\n
\u5185\u5b58<\/td>\n\u2265 256GB<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n\u2265 2TB SSD<\/td>\n<\/tr>\n
\u64cd\u4f5c\u7cfb\u7edf<\/td>\nopenEuler \/ \u9e92\u9e9f \/ \u7edf\u4fe1UOS（\u56fd\u4ea7\u4fe1\u521bOS）<\/td>\n<\/tr>\n
\u9a71\u52a8<\/td>\nCANN \u2265 8.0；Ascend Toolkit \u2265 8.0<\/td>\n<\/tr>\n
\u6846\u67b6\u73af\u5883<\/td>\nMindSpore \u2265 2.3；torch-npu \u2265 2.1（\u517c\u5bb9PyTorch 2.1）<\/td>\n<\/tr>\n
\u90e8\u7f72\u5de5\u5177<\/td>\nXInference \u2265 0.12，Ascend ATC \u5de5\u5177<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e8c\u3001\u6a21\u578b\u6765\u6e90\u4e0e\u57fa\u672c\u7ed3\u6784<\/h3>\n\n\u6a21\u578b\u540d\u79f0\u6765\u6e90\u53c2\u6570\u91cf\u6846\u67b6\u5f53\u524d\u683c\u5f0f\u91cf\u5316\u7c7b\u578b\u7528\u9014<\/tr>\n\n\n\n
Qwen3-32B-Base<\/td>\n\u4ece\u963f\u91cc\u5df4\u5df4\u5b98\u65b9 \/ Hugging Face \u4e0b\u8f7d<\/td>\n32B<\/td>\nPyTorch<\/td>\n.bin \/ .safetensors<\/td>\nFP16 \/ BF16<\/td>\n\u539f\u59cb\u9884\u8bad\u7ec3\u6a21\u578b<\/td>\n<\/tr>\n
Qwen3-32B-Finetuned<\/td>\n\u5728\u6607\u817e910B2\u4e0a\u8fdb\u884c\u5fae\u8c03（\u4f7f\u7528MindSpore\u6846\u67b6）<\/td>\n32B<\/td>\nMindSpore<\/td>\n.ckpt<\/td>\nINT8 \/ BF16<\/td>\n\u884c\u4e1a\u5b9a\u5236\u5fae\u8c03\u6a21\u578b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e09\u3001\u6a21\u578b\u683c\u5f0f\u8f6c\u6362\u6d41\u7a0b<\/h3>\n

\u6a21\u578b\u90e8\u7f72\u524d\u9700\u7edf\u4e00\u683c\u5f0f，\u6607\u817e\u5e73\u53f0\u63a8\u8350\u4f7f\u7528 MindIR \u6216 OM \u683c\u5f0f\u3002<\/p>\n

1\ufe0f\u20e3 \u4ece PyTorch \u683c\u5f0f\u8f6c\u6362\u5230 MindIR \u683c\u5f0f<\/h4>\n

\u65b9\u6cd5 1：\u901a\u8fc7 MindConverter<\/h5>\n

MindConverter \u662f\u6607\u817e\u5b98\u65b9\u63d0\u4f9b\u7684\u8de8\u6846\u67b6\u6a21\u578b\u8f6c\u6362\u5de5\u5177\u3002<\/p>\n

mindconverter –in_framework pytorch \\\\<\/span>
\n –model_file \/models\/qwen32b\/pytorch_model.bin \\\\<\/span>
\n –input_shape '[1,1024]'<\/span> \\\\<\/span>
\n –output_file \/models\/qwen32b\/qwen32b.mindir<\/p>\n

\u65b9\u6cd5 2：\u901a\u8fc7 ONNX \u4e2d\u8f6c<\/h5>\n

\u5982\u679cMindConverter\u8f6c\u6362\u5931\u8d25，\u53ef\u901a\u8fc7ONNX\u4f5c\u4e3a\u4e2d\u95f4\u5c42：<\/p>\n

# PyTorch -> ONNX<\/span>
\nimport<\/span> torch
\ndummy_input =<\/span> torch.<\/span>ones(<\/span>1<\/span>,<\/span> 1024<\/span>,<\/span> dtype=<\/span>torch.<\/span>int64)<\/span>
\ntorch.<\/span>onnx.<\/span>export(<\/span>model,<\/span> dummy_input,<\/span> "qwen32b.onnx"<\/span>,<\/span> opset_version=<\/span>17<\/span>)<\/span><\/p>\n

# ONNX -> MindIR<\/span>
\natc –model=<\/span>qwen32b.onnx \\\\<\/span>
\n –framework=<\/span>5<\/span> \\\\<\/span>
\n –output=<\/span>qwen32b_mindir \\\\<\/span>
\n –soc_version=<\/span>Ascend910B2<\/p>\n

2\ufe0f\u20e3 MindIR \u2192 OM（\u90e8\u7f72\u4f18\u5316\u683c\u5f0f）<\/h4>\n

OM（Offline Model）\u662f\u6607\u817e\u82af\u7247\u7684\u79bb\u7ebf\u6267\u884c\u4f18\u5316\u683c\u5f0f，\u90e8\u7f72\u65f6\u52a0\u8f7d\u6027\u80fd\u66f4\u4f18\u3002<\/p>\n

atc –model=<\/span>qwen32b.mindir \\\\<\/span>
\n –framework=<\/span>1<\/span> \\\\<\/span>
\n –output=<\/span>qwen32b_om \\\\<\/span>
\n –input_format=<\/span>ND \\\\<\/span>
\n –input_shape=<\/span>"input_ids:1,1024"<\/span> \\\\<\/span>
\n –soc_version=<\/span>Ascend910B2 \\\\<\/span>
\n –precision_mode=<\/span>allow_mix_precision<\/p>\n

\u2699\ufe0f \u6ce8\u610f：<\/p>\n

    \n
  • soc_version \u5fc5\u987b\u4e0e\u6607\u817e\u82af\u7247\u578b\u53f7\u4e00\u81f4\u3002<\/li>\n
  • MindIR \u683c\u5f0f\u652f\u6301\u8bad\u7ec3\u548c\u90e8\u7f72；OM \u683c\u5f0f\u4ec5\u652f\u6301\u63a8\u7406\u3002<\/li>\n<\/ul>\n

    \u56db\u3001\u6a21\u578b\u90e8\u7f72\u65b9\u5f0f<\/h3>\n

    \u6211\u4eec\u5e0c\u671b\u90e8\u7f72\u4e24\u4e2a\u6a21\u578b（Base \u4e0e Finetuned），\u5e76\u53ef\u5728 XInference \u4e2d\u52a8\u6001\u5207\u6362\u3002<\/p>\n

    1\ufe0f\u20e3 \u73af\u5883\u76ee\u5f55\u7ed3\u6784<\/h4>\n

    \/models\/
    \n\u2502
    \n\u251c\u2500\u2500 qwen32b_base\/
    \n\u2502 \u251c\u2500\u2500 qwen32b_om.om
    \n\u2502 \u2514\u2500\u2500 config.json
    \n\u2502
    \n\u2514\u2500\u2500 qwen32b_finetune\/
    \n \u251c\u2500\u2500 qwen32b_finetune_om.om
    \n \u2514\u2500\u2500 config.json<\/p>\n

    2\ufe0f\u20e3 \u542f\u52a8 XInference \u670d\u52a1<\/h4>\n

    # \u542f\u52a8 Base \u6a21\u578b<\/span>
    \nxinf start –model Qwen3-32B-Base \\\\<\/span>
    \n –model-path \/models\/qwen32b_base \\\\<\/span>
    \n –device ascend \\\\<\/span>
    \n –precision bf16 \\\\<\/span>
    \n –port 8081<\/span><\/p>\n

    # \u542f\u52a8 Finetuned \u6a21\u578b<\/span>
    \nxinf start –model Qwen3-32B-Finetuned \\\\<\/span>
    \n –model-path \/models\/qwen32b_finetune \\\\<\/span>
    \n –device ascend \\\\<\/span>
    \n –precision int8 \\\\<\/span>
    \n –port 8082<\/span><\/p>\n

    \u2705 \u652f\u6301\u4f7f\u7528 Docker \u5bb9\u5668\u5316\u90e8\u7f72（xinf ascend-runtime \u5b98\u65b9\u955c\u50cf）<\/p>\n

    3\ufe0f\u20e3 \u6a21\u578b\u6ce8\u518c\u914d\u7f6e（xinf.json）<\/h4>\n

    {<\/span>
    \n "models"<\/span>:<\/span> [<\/span>
    \n {<\/span>
    \n "name"<\/span>:<\/span> "Qwen3-32B-Base"<\/span>,<\/span>
    \n "path"<\/span>:<\/span> "\/models\/qwen32b_base\/qwen32b_om.om"<\/span>,<\/span>
    \n "device"<\/span>:<\/span> "ascend"<\/span>,<\/span>
    \n "port"<\/span>:<\/span> 8081<\/span>,<\/span>
    \n "precision"<\/span>:<\/span> "bf16"<\/span>
    \n }<\/span>,<\/span>
    \n {<\/span>
    \n "name"<\/span>:<\/span> "Qwen3-32B-Finetuned"<\/span>,<\/span>
    \n "path"<\/span>:<\/span> "\/models\/qwen32b_finetune\/qwen32b_finetune_om.om"<\/span>,<\/span>
    \n "device"<\/span>:<\/span> "ascend"<\/span>,<\/span>
    \n "port"<\/span>:<\/span> 8082<\/span>,<\/span>
    \n "precision"<\/span>:<\/span> "int8"<\/span>
    \n }<\/span>
    \n ]<\/span>
    \n}<\/span><\/p>\n

    \u4e94\u3001XInference \u6a21\u578b\u5207\u6362\u4e0e\u8c03\u7528<\/h3>\n

    1\ufe0f\u20e3 \u6a21\u578b\u52a8\u6001\u5207\u6362（REST API）<\/h4>\n

    curl<\/span> -X POST http:\/\/localhost:8000\/api\/models\/select \\\\<\/span>
    \n -H "Content-Type: application\/json"<\/span> \\\\<\/span>
    \n -d '{"model_name": "Qwen3-32B-Finetuned"}'<\/span><\/p>\n

    \u5207\u6362\u56de\u539f\u59cb\u6a21\u578b：<\/p>\n

    curl<\/span> -X POST http:\/\/localhost:8000\/api\/models\/select \\\\<\/span>
    \n -d '{"model_name": "Qwen3-32B-Base"}'<\/span><\/p>\n

    2\ufe0f\u20e3 \u63a8\u7406\u8c03\u7528\u793a\u4f8b（Python）<\/h4>\n

    import<\/span> requests<\/p>\n

    def<\/span> query_model<\/span>(<\/span>port,<\/span> prompt)<\/span>:<\/span>
    \n url =<\/span> f"http:\/\/localhost:<\/span>{<\/span>port}<\/span><\/span>\/v1\/completions"<\/span><\/span>
    \n payload =<\/span> {<\/span>
    \n "prompt"<\/span>:<\/span> prompt,<\/span>
    \n "max_tokens"<\/span>:<\/span> 256<\/span>,<\/span>
    \n "temperature"<\/span>:<\/span> 0.8<\/span>
    \n }<\/span>
    \n headers =<\/span> {<\/span>"Content-Type"<\/span>:<\/span> "application\/json"<\/span>}<\/span>
    \n response =<\/span> requests.<\/span>post(<\/span>url,<\/span> headers=<\/span>headers,<\/span> json=<\/span>payload)<\/span>
    \n return<\/span> response.<\/span>json(<\/span>)<\/span>[<\/span>"choices"<\/span>]<\/span>[<\/span>0<\/span>]<\/span>[<\/span>"text"<\/span>]<\/span><\/p>\n

    print<\/span>(<\/span>"Base\u6a21\u578b\u56de\u7b54："<\/span>)<\/span>
    \nprint<\/span>(<\/span>query_model(<\/span>8081<\/span>,<\/span> "\u4ecb\u7ecd\u4e00\u4e0b\u9cb2\u9e4f920\u7684CPU\u67b6\u6784\u4f18\u52bf"<\/span>)<\/span>)<\/span><\/p>\n

    print<\/span>(<\/span>"\\\\nFinetuned\u6a21\u578b\u56de\u7b54："<\/span>)<\/span>
    \nprint<\/span>(<\/span>query_model(<\/span>8082<\/span>,<\/span> "\u89e3\u91ca\u4e00\u4e0b\u4fe1\u521b\u751f\u6001\u4e0bAI\u7b97\u6cd5\u7684\u4f18\u52bf"<\/span>)<\/span>)<\/span><\/p>\n

    \u516d\u3001\u6a21\u578b\u63a8\u7406\u6027\u80fd\u4e0e\u4f18\u5316<\/h3>\n\n\u4f18\u5316\u624b\u6bb5\u8bf4\u660e\u6548\u679c<\/tr>\n\n\n\n\n\n
    INT8\u91cf\u5316<\/td>\n\u5fae\u8c03\u6a21\u578b\u5728\u5bfc\u51fa\u65f6\u4f7f\u7528\u91cf\u5316\u611f\u77e5\u8bad\u7ec3（QAT）<\/td>\n\u663e\u5b58\u964d\u4f4e\u7ea6 50%，\u901f\u5ea6\u63d0\u5347\u7ea6 1.6\u00d7<\/td>\n<\/tr>\n
    \u53cc\u5361\u5e76\u884c\u63a8\u7406<\/td>\n\u4f7f\u7528\u4e24\u5f20910B2\u5361\u5206\u522b\u90e8\u7f72\u4e24\u4e2a\u6a21\u578b<\/td>\n\u5b9e\u73b0\u6a21\u578b\u5e76\u884c\u8c03\u7528<\/td>\n<\/tr>\n
    Pipeline\u5207\u5206<\/td>\n\u6a21\u578b\u524d\u5411\u4f20\u64ad\u5206\u6bb5\u6267\u884c<\/td>\n\u9002\u5408\u663e\u5b58\u4e0d\u8db3\u60c5\u51b5<\/td>\n<\/tr>\n
    XInference\u5f02\u6b65\u961f\u5217<\/td>\n\u652f\u6301\u591a\u8bf7\u6c42\u5e76\u53d1\u5206\u53d1<\/td>\n\u63d0\u5347\u541e\u5410\u91cf 2~3\u500d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u4e03\u3001\u5e38\u89c1\u95ee\u9898\u4e0e\u89e3\u51b3\u5efa\u8bae<\/h3>\n\n\u95ee\u9898\u53ef\u80fd\u539f\u56e0\u89e3\u51b3\u65b9\u6848<\/tr>\n\n\n\n\n\n
    CANN\u4e0d\u652f\u6301\u8be5\u7b97\u5b50<\/td>\n\u8f6c\u6362\u65f6ONNX\u7b97\u5b50\u4e0d\u517c\u5bb9<\/td>\n\u5728atc\u547d\u4ee4\u4e2d\u52a0–customize_op \u6216\u4f7f\u7528MindSpore\u539f\u751f\u6a21\u578b<\/td>\n<\/tr>\n
    \u5185\u5b58\u4e0d\u8db3<\/td>\n\u6a21\u578b\u663e\u5b58\u5f00\u9500\u5927<\/td>\n\u5f00\u542fINT8\u91cf\u5316\u6216\u5206\u5c42\u52a0\u8f7d<\/td>\n<\/tr>\n
    XInference\u65e0\u6cd5\u52a0\u8f7d<\/td>\n\u6a21\u578b\u8def\u5f84\u6216\u683c\u5f0f\u4e0d\u5339\u914d<\/td>\n\u786e\u8ba4.om\u6587\u4ef6\u8def\u5f84\u4e0e\u914d\u7f6e\u6587\u4ef6\u4e00\u81f4<\/td>\n<\/tr>\n
    \u8bf7\u6c42\u5ef6\u8fdf\u9ad8<\/td>\n\u540c\u6b65\u963b\u585e<\/td>\n\u4f7f\u7528\u5f02\u6b65\u8c03\u7528API\u6216\u591a\u8fdb\u7a0b\u90e8\u7f72<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u516b\u3001\u603b\u7ed3<\/h3>\n\n\u9879\u76ee\u63a8\u8350\u65b9\u6848\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n
    \u6a21\u578b\u8bad\u7ec3\u6846\u67b6<\/td>\nMindSpore（\u6607\u817e\u539f\u751f）<\/td>\n\u5fae\u8c03\u9636\u6bb5\u6548\u7387\u9ad8<\/td>\n<\/tr>\n
    \u6a21\u578b\u90e8\u7f72\u683c\u5f0f<\/td>\nMindIR \/ OM<\/td>\n\u539f\u751f\u652f\u6301\u6607\u817e\u63a8\u7406<\/td>\n<\/tr>\n
    \u591a\u6a21\u578b\u8c03\u7528<\/td>\nXInference \u52a8\u6001\u6ce8\u518c\u5207\u6362<\/td>\n\u5feb\u901f\u5728Base\u4e0eFinetune\u6a21\u578b\u95f4\u5207\u6362<\/td>\n<\/tr>\n
    \u6027\u80fd\u4f18\u5316<\/td>\nINT8\u91cf\u5316 + \u53cc\u5361\u5e76\u53d1 + \u5f02\u6b65\u63a8\u7406<\/td>\n\u6ee1\u8db3\u4fe1\u521b\u786c\u4ef6\u7ea6\u675f<\/td>\n<\/tr>\n
    \u517c\u5bb9\u6027<\/td>\n\u652f\u6301PyTorch\u3001MindSpore\u3001ONNX\u7edf\u4e00\u90e8\u7f72<\/td>\n\u9002\u914d\u7075\u6d3b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

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