{"id":1241,"date":"2024-04-28T11:32:42","date_gmt":"2024-04-28T11:32:42","guid":{"rendered":"https:\/\/www.nicekj.com\/?p=1241"},"modified":"2024-04-28T11:33:42","modified_gmt":"2024-04-28T11:33:42","slug":"jinyong61xingdaimaniyenengconglingxunliandamoxing","status":"publish","type":"post","link":"https:\/\/www.nicekj.com\/jinyong61xingdaimaniyenengconglingxunliandamoxing.html","title":{"rendered":"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b"},"content":{"rendered":"

\n

\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n

\n

\"\u52a8\u56fe\u5c01\u9762\"<\/figure>\n<\/p>\n

\u817e\u5c0f\u4e91\u5bfc\u8bfb<\/p>\n

\u672c\u6587\u5e76\u975e\u57fa\u4e8e\u5fae\u8c03\u8bad\u7ec3\u6a21\u578b\uff0c\u800c\u662f\u4ece\u5934\u5f00\u59cb\u8bad\u7ec3\u51fa\u4e00\u4e2a\u5168\u65b0\u7684\u5927\u8bed\u8a00\u6a21\u578b\u7684\u786c\u6838\u6559\u7a0b\u3002\u770b\u5b8c\u672c\u7bc7\uff0c\u4f60\u5c06\u4e86\u89e3\u8bad\u7ec3\u51fa\u4e00\u4e2a\u5927\u6a21\u578b\u7684\u73af\u5883\u51c6\u5907\u3001\u6570\u636e\u51c6\u5907\uff0c\u751f\u6210\u5206\u8bcd\uff0c\u6a21\u578b\u8bad\u7ec3\u3001\u6d4b\u8bd5\u6a21\u578b\u7b49\u73af\u8282\u5206\u522b\u9700\u8981\u505a\u4ec0\u4e48\u3002AI \u5c0f\u767d\u53cb\u597d~\u6587\u4e2d\u4ee3\u7801\u53ef\u4ee5\u76f4\u63a5\u5b9e\u64cd\u8fd0\u884c\u3002\u6b22\u8fce\u9605\u8bfb\u4f53\u9a8c\u3002<\/p>\n

\u76ee\u5f55<\/p>\n

1 \u51c6\u5907\u8bad\u7ec3\u73af\u5883<\/p>\n

2 \u51c6\u5907\u8bad\u7ec3\u6570\u636e<\/p>\n

3 \u8bad\u7ec3\u5206\u8bcd\u5668<\/p>\n

4 \u8bad\u7ec3\u6a21\u578b<\/p>\n

5 \u6d4b\u8bd5\u6a21\u578b<\/p>\n

6 \u5b8c\u6574\u4ee3\u7801<\/p>\n

\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u4f60\u53ef\u4ee5\u9884\u8bad\u7ec3\u4e00\u4e2a\u5168\u65b0\u5927\u8bed\u8a00\u6a21\u578b\u3002<\/p>\n

\u6ce8\u610f\u662f\u5168\u65b0\u7684\u6a21\u578b\uff0c\u4e0d\u662f\u5fae\u8c03\u3002<\/strong><\/p>\n

\u5168\u65b0\u8bad\u7ec3\u7684\u597d\u5904\u662f\u8bad\u7ec3\u7684\u6570\u636e\u3001\u8bad\u7ec3\u7684\u53c2\u6570\u90fd\u662f\u53ef\u4fee\u6539\u7684\uff0c\u901a\u8fc7\u8c03\u8bd5\u8fd0\u884c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u7684\u7406\u89e3\u5927\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u3002\u6211\u4eec\u53ef\u4ee5\u7528\u7279\u5b9a\u7c7b\u578b\u6570\u636e\u7684\u8bad\u7ec3\uff0c\u6765\u5b8c\u6210\u7279\u5b9a\u7c7b\u578b\u6570\u636e\u7684\u8f93\u51fa\u3002<\/p>\n

\u5173\u4e8e\u5927\u6a21\u578b\u5df2\u7ecf\u6709\u5f88\u591a\u6587\u7ae0\uff0c\u5fae\u8c03\u6a21\u578b\u7684\u6587\u7ae0\u6bd4\u8f83\u591a\uff0c\u5168\u65b0\u9884\u8bad\u7ec3\u5168\u65b0\u6a21\u578b\u7684\u6587\u7ae0\u5f88\u5c11\u3002\u4e2a\u4eba\u89c9\u5f97\u6709\u7684\u4e5f\u8bb2\u7684\u5f88\u590d\u6742\uff0c\u4ee3\u7801\u4e5f\u5f88\u96be\u8dd1\u901a\u3002\u672c\u6587\u4e0d\u4f1a\u8bb2\u7684\u5f88\u590d\u6742\uff0c\u4ee3\u7801\u4e5f\u5f88\u5bb9\u6613\u8fd0\u884c\u3002\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u5c31\u80fd\u8bad\u7ec3\u51fa\u4e00\u4e2a\u5168\u65b0\u5927\u8bed\u8a00\u6a21\u578b\u3002<\/p>\n

\n

\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n

\u672c\u6587\u4ee5\u4ee3\u7801\u4e3a\u4e3b\uff0c\u8fd0\u884c\u4ee3\u7801\u9700\u8981 Python \u73af\u5883\u3002<\/p>\n

01\u3001\u51c6\u5907\u8bad\u7ec3\u73af\u5883<\/h2>\n

\u6211\u7684\u8bad\u7ec3\u73af\u5883\u57fa\u4e8e\u817e\u8baf\u4e91\u7684 GPU \u673a\u5668\u3002<\/p>\n

\u5730\u5740\uff1acloud.tencent.com\/product\/gpu<\/a><\/p>\n\n\n\n\n\n\n
GPU\u7c7b\u578b\uff1aGN7.2XLARGE32 T4 \u663e\u5361\uff1a1\u9897 \u663e\u5b58\uff1a16GB\uff1b python 3.11\uff1b requirements.txt:<\/th><\/tr><\/thead><\/table>\n
<\/div>
ini<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>tokenizers<\/span>==0.13<\/span>.3<\/span><\/span>\ntorch<\/span>==2.0<\/span>.1<\/span><\/span>\ntransformers<\/span>==4.30<\/span>.<\/span>\n<\/code><\/pre>\n
02\u3001\u51c6\u5907\u8bad\u7ec3\u6570\u636e<\/h2>\n
\u9996\u5148\u6211\u4eec\u8981\u4e3a\u8bad\u7ec3\u51c6\u5907\u6570\u636e\uff0c\u6bd4\u5982\u6211\u5c31\u60f3\u57fa\u4e8e\u300a\u4e09\u56fd\u6f14\u4e49\u300b\u8bad\u7ec3\u4e00\u4e2a\u6a21\u578b\u3002\u4e09\u56fd\u6f14\u4e49\u4e0b\u8f7d\u5730\u5740\uff1a<\/p>\n
raw.githubusercontent.com\/xinzhanguo\/\u2026<\/a><\/p>\n
\n
\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n
03\u3001\u8bad\u7ec3\u5206\u8bcd\u5668<\/h2>\n
\u5206\u8bcd\uff08tokenization\uff09\u662f\u628a\u8f93\u5165\u6587\u672c\u5207\u5206\u6210\u6709\u610f\u4e49\u7684\u5b50\u5355\u5143\uff08tokens\uff09\u3002\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\uff0c\u6839\u636e\u6211\u4eec\u7684\u6570\u636e\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u5206\u8bcd\u5668\uff1a<\/p>\n
<\/div>
python<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> tokenizers import<\/span> Tokenizer<\/span>\nfrom<\/span> tokenizers.models import<\/span> BPE<\/span>\nfrom<\/span> tokenizers.trainers import<\/span> BpeTrainer<\/span>\nfrom<\/span> tokenizers.normalizers import<\/span> NFKC, Sequence<\/span><\/span>\nfrom<\/span> tokenizers.pre_tokenizers import<\/span> ByteLevel<\/span>\nfrom<\/span> tokenizers.decoders import<\/span> ByteLevel as<\/span> ByteLevelDecoder<\/span>\nfrom<\/span> transformers import<\/span> GPT2TokenizerFast<\/span>\n<\/span>\n# \u6784\u5efa\u5206\u8bcd\u5668 GPT2 \u57fa\u4e8e BPE \u7b97\u6cd5\u5b9e\u73b0<\/span><\/span>\ntokenizer = Tokenizer(BPE(unk_token=\"<unk>\"<\/span>))<\/span>\ntokenizer.normalizer = Sequence<\/span>([NFKC()])<\/span>\ntokenizer.pre_tokenizer = ByteLevel()<\/span>\ntokenizer.decoder = ByteLevelDecoder()<\/span>\n<\/span>\nspecial_tokens = [\"<s>\"<\/span>,\"<pad>\"<\/span>,\"<\/s>\"<\/span>,\"<unk>\"<\/span>,\"<mask>\"<\/span>]<\/span>\ntrainer = BpeTrainer(vocab_size=50000<\/span>, show_progress=True<\/span>, inital_alphabet=ByteLevel.alphabet(), special_tokens=special_tokens)<\/span>\n# \u521b\u5efa text \u6587\u4ef6\u5939\uff0c\u5e76\u628a sanguoyanyi.txt \u4e0b\u8f7d\uff0c\u653e\u5230\u76ee\u5f55\u91cc<\/span><\/span>\nfiles = [\"text\/sanguoyanyi.txt\"<\/span>]<\/span>\n# \u5f00\u59cb\u8bad\u7ec3\u4e86<\/span><\/span>\ntokenizer.train(files, trainer)<\/span>\n# \u628a\u8bad\u7ec3\u7684\u5206\u8bcd\u901a\u8fc7GPT2\u4fdd\u5b58\u8d77\u6765\uff0c\u4ee5\u65b9\u4fbf\u540e\u7eed\u4f7f\u7528<\/span><\/span>\nnewtokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)<\/span>\nnewtokenizer.save_pretrained(\".\/sanguo\"<\/span>)<\/span>\n<\/code><\/pre>\n
\u8fd0\u884c\u65f6\u663e\u793a\u5982\u4e0b\u56fe\uff1a<\/p>\n
\n
\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n
\u6210\u529f\u8fd0\u884c\u4ee3\u7801\u540e\uff0c\u6211\u4eec\u5728 sanguo \u76ee\u5f55\u751f\u6210\u5982\u4e0b\u6587\u4ef6\uff1a<\/p>\n
<\/div>
<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>merges.txt<\/span>\nspecial_tokens_map.json<\/span>\ntokenizer.json<\/span>\ntokenizer_config.json<\/span>\nvocab.json<\/span>\n<\/code><\/pre>\n
\u73b0\u5728\u6211\u4eec\u5df2\u7ecf\u6210\u529f\u8bad\u7ec3\u4e86\u4e00\u4e2a\u5927\u8bed\u8a00\u6a21\u578b\u7684\u5206\u8bcd\u5668\u3002<\/p>\n
04\u3001\u8bad\u7ec3\u6a21\u578b<\/h2>\n
\u5229\u7528\u4e0b\u9762\u4ee3\u7801\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\uff1a<\/p>\n
<\/div>
ini<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer<\/span>\n# \u52a0\u8f7d\u5206\u8bcd\u5668<\/span><\/span>\ntokenizer<\/span> = GPT2Tokenizer.from_pretrained(\".\/sanguo\"<\/span>)<\/span>\ntokenizer.add_special_tokens({<\/span>\n  \"eos_token\": \"<\/s>\",<\/span>\n  \"bos_token\": \"<s>\",<\/span>\n  \"unk_token\": \"<unk>\",<\/span>\n  \"pad_token\": \"<pad>\",<\/span>\n  \"mask_token\": \"<mask>\"<\/span>\n})<\/span>\n# \u914d\u7f6eGPT2\u6a21\u578b\u53c2\u6570<\/span><\/span>\nconfig<\/span> = GPT2Config(<\/span>\n  vocab_size<\/span>=tokenizer.vocab_size,<\/span>\n  bos_token_id<\/span>=tokenizer.bos_token_id,<\/span>\n  eos_token_id<\/span>=tokenizer.eos_token_id<\/span>\n)<\/span>\n# \u521b\u5efa\u6a21\u578b<\/span><\/span>\nmodel<\/span> = GPT2LMHeadModel(config)<\/span>\n# \u8bad\u7ec3\u6570\u636e\u6211\u4eec\u7528\u6309\u884c\u5206\u5272<\/span><\/span>\nfrom transformers import LineByLineTextDataset<\/span>\ndataset<\/span> = LineByLineTextDataset(<\/span>\n    tokenizer<\/span>=tokenizer,<\/span>\n    file_path<\/span>=\".\/text\/sanguoyanyi.txt\"<\/span>,<\/span>\n    block_size<\/span>=32<\/span>,<\/span>\n  # \u5982\u679c\u8bad\u7ec3\u65f6\u4f60\u7684\u663e\u5b58\u4e0d\u591f<\/span><\/span>\n  # \u53ef\u4ee5\u9002\u5f53\u8c03\u5c0f block_size<\/span><\/span>\n)<\/span>\nfrom transformers import DataCollatorForLanguageModeling<\/span>\ndata_collator<\/span> = DataCollatorForLanguageModeling(<\/span>\n    tokenizer<\/span>=tokenizer, mlm=False<\/span>, mlm_probability=0.15<\/span><\/span>\n)<\/span>\n<\/span>\nfrom transformers import Trainer, TrainingArguments<\/span>\n# \u914d\u7f6e\u8bad\u7ec3\u53c2\u6570<\/span><\/span>\ntraining_args<\/span> = TrainingArguments(<\/span>\n    output_dir<\/span>=\".\/output\"<\/span>,<\/span>\n    overwrite_output_dir<\/span>=True<\/span>,<\/span>\n    num_train_epochs<\/span>=20<\/span>,<\/span>\n    per_gpu_train_batch_size<\/span>=16<\/span>,<\/span>\n    save_steps<\/span>=2000<\/span>,<\/span>\n    save_total_limit<\/span>=2<\/span>,<\/span>\n)<\/span>\ntrainer<\/span> = Trainer(<\/span>\n    model<\/span>=model,<\/span>\n    args<\/span>=training_args,<\/span>\n    data_collator<\/span>=data_collator,<\/span>\n    train_dataset<\/span>=dataset,<\/span>\n)<\/span>\ntrainer.train()<\/span>\n# \u4fdd\u5b58\u6a21\u578b<\/span><\/span>\nmodel.save_pretrained('.\/sanguo')<\/span>\n<\/code><\/pre>\n
\u8fd0\u884c\u6bd4\u8f83\u8017\u65f6\uff0c\u663e\u793a\u8bad\u7ec3\u6570\u636e\u5982\u4e0b\u56fe\uff1a<\/p>\n
\n
\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n
\u6210\u529f\u8fd0\u884c\u4ee3\u7801\uff0c\u6211\u4eec\u53d1\u73b0 sanguo \u76ee\u5f55\u4e0b\u9762\u591a\u4e86\u4e09\u4e2a\u6587\u4ef6\uff1a<\/p>\n
<\/div>
arduino<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>config.json<\/span>\ngeneration_config.json<\/span>\npytorch_model.bin<\/span>\n<\/code><\/pre>\n
\u73b0\u5728\u6211\u4eec\u5c31\u6210\u529f\u751f\u6210\u8bad\u7ec3\u51fa\u57fa\u4e8e\u300a\u4e09\u56fd\u6f14\u4e49\u300b\u7684\u4e00\u4e2a\u5927\u8bed\u8a00\u6a21\u578b\u3002<\/p>\n
05\u3001\u6d4b\u8bd5\u6a21\u578b<\/h2>\n
\u6211\u4eec\u7528\u6587\u672c\u751f\u6210\uff0c\u5bf9\u6a21\u578b\u8fdb\u884c\u6d4b\u8bd5\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
<\/div>
scss<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from transformers import pipeline, set_seed<\/span>\ngenerator = pipeline<\/span>('text-generation', model='.\/sanguo')<\/span>\nset_seed<\/span>(42<\/span>)<\/span>\ntxt = generator<\/span>(\"\u5415\u5e03\", max_length=10<\/span>)<\/span>\nprint<\/span>(txt)<\/span>\n<\/code><\/pre>\n
\u8fd0\u884c\u663e\u793a\u6a21\u578b\u8f93\u51fa\u4e86\u4e09\u56fd\u76f8\u5173\u7684\u6587\u672c\uff1a\u201c\u5415\u5e03\u5341\u4e8c\u56de \u5f20\u7ffc\u5fb7 \u5f20\u7ffc\u5fb7\u65f6\u66f9\u64cd \u6b66\u4faf\u8ba1\u8ba1\u8ba1\u201d<\/p>\n
\n
\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n
\u518d\u6d4b\u8bd5\u4e00\u6761\uff1a<\/p>\n
<\/div>
scss<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>txt = generator<\/span>(\"\u63a5\u7740\u594f\u4e50\", max_length=10<\/span>)<\/span>\nprint<\/span>(txt)<\/span>\n<\/code><\/pre>\n
“\u63a5\u7740\u594f\u4e50u3000\u5374\u8bf4\u66f9\u64cd\u5f15\u519b\u56e0\u4e8c\u4eba”<\/p>\n
\n
\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n
\u8fd9\u5185\u5bb9\u4e0d\u5fcd\u76f4\u89c6\uff0c\u5982\u679c\u60f3\u4f18\u5316\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u57fa\u4e8e\u5168\u65b0\u7684\u6a21\u578b\u8fdb\u884c\u5fae\u8c03\u8bad\u7ec3\uff1b\u6211\u4eec\u4e5f\u53ef\u4ee5\u9002\u5f53\u5730\u8c03\u6574\u4e0b\u8bad\u7ec3\u53c2\u6570\uff0c\u4ee5\u8fbe\u5230\u8f83\u597d\u7684\u6548\u679c\u3002<\/p>\n
06\u3001\u5b8c\u6574\u4ee3\u7801<\/h2>\n
\u4ee5\u4e0b\u662f\u5b8c\u6574\u4ee3\u7801\uff0c\u4ee3\u7801\u5730\u5740\uff1a<\/p>\n
github.com\/xinzhanguo\/\u2026<\/a><\/p>\n
linux \u4e2d\u8fd0\u884c\u65b9\u6cd5\uff1a<\/p>\n
<\/div>
bash<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div># \u521b\u5efa\u73af\u5883<\/span><\/span>\npython3 -m venv ~\/.env<\/span>\n# \u52a0\u8f7d\u73af\u5883<\/span><\/span>\nsource<\/span> ~\/.env\/bin\/activate<\/span>\n# \u4e0b\u8f7d\u4ee3\u7801<\/span><\/span>\ngit clone<\/span> git@github.com:xinzhanguo\/hellollm.git<\/span>\ncd<\/span> hellollm<\/span>\n# \u5b89\u88c5\u4f9d\u8d56<\/span><\/span>\npip install -r requirements.txt<\/span>\n# \u8fd0\u884c\u4ee3\u7801<\/span><\/span>\npython sanguo.py<\/span>\n<\/code><\/pre>\n
\u4ee5\u4e0a\u6211\u4eec\u5c31\u5b8c\u6210\u4e00\u4e2a\u5168\u65b0\u7684\u6a21\u578b\u8bad\u7ec3\u3002\u4ee3\u7801\u53bb\u9664\u6ce8\u91ca\u7a7a\u884c\u603b\u517161\u884c\u3002<\/p>\n
\u672c\u6587\u4ee3\u7801\u6a21\u578b\u662f\u57fa\u4e8e GPT2 \u7684\uff0c\u5f53\u7136\u4f60\u4e5f\u53ef\u4ee5\u57fa\u4e8e LLama \u6216\u8005 Bert \u7b49\u6a21\u578b\u53bb\u5b9e\u73b0\u5168\u65b0\u7684\u5927\u8bed\u8a00\u6a21\u578b\u3002<\/p>\n
\u4ee3\u7801\u867d\u7136\u4e0d\u662f\u5f88\u591a\uff0c\u4f46\u662f\u5982\u679c\u521d\u6b21\u5c1d\u8bd5\u8fd0\u884c\u7684\u8bdd\u4f60\u4e5f\u8bb8\u4f1a\u9047\u5230\u5f88\u591a\u95ee\u9898\uff0c\u6bd4\u5982\u73af\u5883\u642d\u5efa\u3002\u4e3a\u4e86\u907f\u514d\u5176\u4ed6\u70e6\u607c\uff0c\u6211\u5efa\u8bae\u7528 docker \u65b9\u5f0f\u8fd0\u884c\u4ee3\u7801:<\/p>\n
<\/div>
bash<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div># \u4e0b\u8f7d\u4ee3\u7801<\/span><\/span>\ngit clone<\/span> git@github.com:xinzhanguo\/hellollm.git<\/span>\ncd<\/span> hellollm<\/span>\n# \u7f16\u8bd1\u955c\u50cf<\/span><\/span>\ndocker build -t hellollm:beta .<\/span>\n# \u53ef\u4ee5\u9009\u62e9\u4ee5GPU\u65b9\u5f0f\u8fd0\u884c<\/span><\/span>\n# docker run -it --gpus all hellollm:beta sh<\/span><\/span>\ndocker run -it hellollm:beta sh<\/span>\npython sanguo.py<\/span>\n<\/code><\/pre>\n
\u66f4\u591a\u4ee3\u7801\u53ef\u4ee5\u53c2\u8003\uff1aHello LLM!<\/p>\n
\u4ee5\u4e0a\u5c31\u662f\u672c\u7bc7\u6587\u7ae0\u7684\u5168\u90e8\u5185\u5bb9\uff0c\u6b22\u8fce\u8f6c\u53d1\u5206\u4eab\u3002<\/p>\n
-End-<\/p>\n
\u539f\u521b\u4f5c\u8005\uff5c\u8f9b\u5360\u56fd<\/p>\n
\u6280\u672f\u8d23\u7f16\uff5cjipingjia<\/p>\n
\n
\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>\n
\u4f60\u89c9\u5f97\u5927\u6a21\u578b\u6709\u54ea\u4e9b\u5e94\u7528\u573a\u666f\uff1f\u6b22\u8fce\u5728\u817e\u8baf\u4e91\u5f00\u53d1\u8005\u516c\u4f17\u53f7<\/a>\u8bc4\u8bba\u533a\u8ba8\u8bba\u3002\u6211\u4eec\u5c06\u9009\u53d61\u5219\u6700\u6709\u610f\u4e49\u7684\u5206\u4eab\uff0c\u9001\u51fa\u817e\u8baf\u4e91\u5f00\u53d1\u8005 – \u9a6c\u514b\u676f1\u4e2a\uff08\u89c1\u4e0b\u56fe\uff09\u30027\u670824\u65e5\u4e2d\u534812\u70b9\u5f00\u5956\u3002<\/p>\n
\n
\"\u4ec5\u752861\u884c\u4ee3\u7801\uff0c\u4f60\u4e5f\u80fd\u4ece\u96f6\u8bad\u7ec3\u5927\u6a21\u578b\"<\/figure>\n<\/p>","protected":false},"excerpt":{"rendered":"
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github.com\/xinzhanguo\/\u2026<\/a><\/p>\n