{"id":5596,"date":"2024-08-01T22:47:02","date_gmt":"2024-08-01T14:47:02","guid":{"rendered":"https:\/\/xinchewhd.com.cn\/?p=5596"},"modified":"2024-08-02T16:11:10","modified_gmt":"2024-08-02T08:11:10","slug":"llama-factory-%e8%ae%a9%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%be%ae%e8%b0%83%e5%8f%98%e5%be%97%e6%9b%b4%e7%ae%80%e5%8d%95%ef%bc%81%ef%bc%81","status":"publish","type":"post","link":"https:\/\/xinchewhd.com.cn\/index.php\/uncategorized\/llama-factory-%e8%ae%a9%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%be%ae%e8%b0%83%e5%8f%98%e5%be%97%e6%9b%b4%e7%ae%80%e5%8d%95%ef%bc%81%ef%bc%81\/","title":{"rendered":"LLaMA-Factory \u8ba9\u5927\u6a21\u578b\u5fae\u8c03\u53d8\u5f97\u66f4\u7b80\u5355\uff01\uff01"},"content":{"rendered":"
\n

\u25bc<\/span>\u6700\u8fd1\u76f4\u64ad\u8d85\u7ea7\u591a\uff0c<\/span>\u9884\u7ea6<\/strong><\/span>\u4fdd\u4f60\u6709\u6536\u83b7<\/span><\/p>\n

<\/section>\n

\u2014<\/em>0<\/strong><\/em><\/span>\u2014<\/em><\/span><\/p>\n

\u80cc\u666f<\/span><\/strong><\/p>\n

\u5982\u679c<\/span>\u53ea\u9700\u8981<\/span>\u6784\u5efa\u4e00\u4efd\u4efb\u52a1\u76f8\u5173\u7684\u6570\u636e\uff0c\u5c31\u53ef\u4ee5\u8f7b\u677e\u901a\u8fc7\u7f51\u9875\u754c\u9762<\/span>\u7684\u5f62\u5f0f\u8fdb\u884c Fine-tuning <\/span>\u5fae\u8c03<\/span>\u64cd\u4f5c\uff0c \u90a3\u4e48\u5fc5\u5c06<\/span>\u5927\u5927\u51cf\u8f7b\u5fae\u8c03\u5de5\u4f5c\u91cf<\/span>\u3002<\/span><\/p>\n

\u4eca\u5e74\u7684 ACL 2024\u89c1\u8bc1\u4e86\u5317\u822a\u548c\u5317\u5927\u5408\u4f5c\u7684\u7a81\u7834\u2014\u8bba\u6587\u300aLLAMAFACTORY: \u7edf\u4e00\u9ad8\u6548\u5fae\u8c03\u8d85\u767e\u79cd\u8bed\u8a00\u6a21\u578b\u300b\u3002\u4ed6\u4eec\u6253\u9020\u7684 LLaMA-Factory\uff0c\u96c6\u6210\u4e86\u9876\u5c16\u7684\u8bad\u7ec3\u6548\u7387\u6280\u672f\uff0c\u8ba9\u7528\u6237\u80fd\u8f7b\u677e\u901a\u8fc7\u7f51\u9875\u754c\u9762 LLAMABOARD\uff0c\u65e0\u987b\u7f16\u7801\uff0c\u5373\u5bf9\u4e0a\u767e\u4e2a\u8bed\u8a00\u6a21\u578b\u8fdb\u884c\u4e2a\u6027\u5316\u5fae\u8c03\u3002<\/span><\/span><\/p>\n

\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u9879\u76ee\u5728\u8bba\u6587\u53d1\u8868\u524d\uff0cGitHub \u4e0a\u5df2\u83b7 13,000 Stars \u548c 1,600 forks\uff0c\u5f70\u663e\u4e86\u5176\u5de8\u5927\u5f71\u54cd\u529b\u3002<\/span><\/span><\/p>\n

\u8bba\u6587\u94fe\u63a5<\/span><\/strong>\uff1ahttps:\/\/arxiv.org\/pdf\/2403.13372<\/span><\/section>\n
GitHub \u94fe\u63a5<\/span><\/strong>\uff1ahttps:\/\/github.com\/hiyouga\/LLaMA-Factory<\/span><\/section>\n

<\/section>\n

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\u2014<\/em>1<\/strong><\/em><\/span>\u2014<\/em><\/span><\/p>\n

\u5fae\u8c03\u754c\u9762\u9884\u89c8\uff08LLaMA Board\uff09<\/span><\/strong><\/span><\/strong><\/p>\n

<\/section>\n
\u53ea\u9700\u4e00\u952e\u542f\u52a8 WebUI\uff0c\u5373\u53ef\u63a5\u5165 LLaMA Board\uff08\u5982\u4e0b\u56fe\u6240\u793a\uff09\uff0c\u8fd9\u662f\u4e00\u4e2a\u76f4\u89c2\u7684\u64cd\u4f5c\u9762\u677f\uff0c\u8ba9\u4f60\u8f7b\u677e\u9009\u62e9\u6570\u636e\u96c6\u3001\u81ea\u5b9a\u4e49\u5404\u79cd\u53c2\u6570\u3002<\/span>\u4f53\u9a8c\u4ece\u9884\u89c8\u914d\u7f6e\u3001\u4fdd\u5b58\u8bad\u7ec3\u8bbe\u7f6e\u5230\u52a0\u8f7d\u914d\u7f6e\u3001\u542f\u52a8\u9ad8\u6548\u8bad\u7ec3\u7684\u65e0\u7f1d\u6d41\u7a0b\u3002<\/span>\u5e76\u4e14\uff0c\u754c\u9762\u5b9e\u65f6\u5c55\u793a\u8bad\u7ec3\u635f\u5931\u66f2\u7ebf\uff0c\u8ba9\u4f60\u968f\u65f6\u638c\u63e1\u5fae\u8c03\u8fdb\u5ea6\u4e0e\u6548\u679c\u3002<\/span><\/section>\n

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\u2014<\/em>2<\/strong><\/em><\/span>\u2014<\/em><\/span><\/p>\n

\u5982\u4f55\u51c6\u5907\u5fae\u8c03\u73af\u5883\uff1f<\/span><\/strong><\/p>\n

<\/section>\n
\u4f53\u9a8c\u4e00\u4e0b\u6b64<\/span>\u6570\u5b57\u4eba\u7684\u751f\u52a8\u6f14\u793a\uff0c\u5b83\u80fd\u806a\u660e\u5730\u7406\u89e3\u5e76\u56de\u5e94\u4f60\u7684\u6bcf\u4e00\u53e5\u8bdd\u3002<\/span>\u76ee\u524d\u7684\u5c0f\u9057\u61be\u662f\uff0c\u5b83\u7684\u601d\u8003\u65f6\u95f4\u7ea6\u4e3a3\u52304\u79d2\uff0c\u4f46\u8fd9\u5bf9\u8bdd\u7ed3\u675f\u540e\u624d\u7ed9\u51fa\u53cd\u5e94\u3002<\/span>\u522b\u62c5\u5fc3\uff0c\u6280\u672f\u7684\u8fdb\u6b65\u5c31\u5728\u773c\u524d\uff0c\u968f\u7740<\/span>\u6a21\u578b\u4f18\u5316\uff0c\u8fd9\u4e00\u7b49\u5f85\u65f6\u95f4\u5373\u5c06\u6210\u4e3a\u8fc7\u53bb\u3002<\/span><\/section>\n
\u7b2c\u4e00\u6b65\u3001\u5b89\u88c5\u73af\u5883\uff08\u4ee5 Conda \u73af\u5883\u4e3a\u4f8b\uff09<\/span><\/strong><\/section>\n
\n
\u7b2c1\u6b65 clone LLaMA-Factory\uff1agit clone --depth1https:\/\/github.com\/hiyouga\/LLaMA-Factory.git<\/span><\/span><\/code>\u7b2c2\u6b65\u8fdb\u5165\u76ee\u5f55\uff1acd LLaMA-Factory<\/span><\/code>\u7b2c3\u6b65\u521b\u5efa\u57fa\u7840\u73af\u5883\uff1aconda create<\/span>-n<\u4f60\u7684\u73af\u5883\u540d\u79f0>python=3.10<\/span><\/span><\/code>\u7b2c4<\/span>\u6b65\u8fdb\u5165conda\u73af\u5883\uff1aconda activate<\/span> <\u4f60\u7684\u73af\u5883\u540d\u79f0><\/span><\/code>\u7b2c5<\/span>\u6b65\u5b89\u88c5\u4f9d\u8d56\uff1apip install<\/span>-e.[metrics]-ihttps:\/\/pypi.tuna.tsinghua.edu.cn\/simple<\/span><\/code><\/code>\u3010\u4e0a\u9762\u8d70\u5b8c\u5373\u53ef\uff0c\u4e0b\u9762\u770b\u4f60\u9700\u8981\u3011<\/span><\/code>\u5982\u679c\u4f60\u9700\u8981deepspeed\u52a0\u901f\uff0c\u518d\u5b89\u88c5\u4e00\u4e2a\u5305\uff1apip install<\/span>deepspeed==0.14<\/span>.3<\/span><\/span><\/code><\/pre>\n<\/section>\n
\u7b2c\u4e8c\u6b65\u3001\u6784\u9020\u6570\u636e\uff0c\u5982\u4e0b\u6240\u793a<\/span><\/strong><\/section>\n
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[<\/span><\/code>    {<\/span><\/code>\"id\"<\/span>: \"p1\"<\/span>,<\/span><\/code>\"system\"<\/span>:\"\u4f60\u662f\u4e00\u4e2a\u9500\u552e\u5c0f\u52a9\u624b\uff0c\u4f60\u7684\u4efb\u52a1\u662f\u9080\u8bf7\u7a0b\u5e8f\u5458\u8d2d\u4e70AI\u5927\u6a21\u578b\u8bfe\u7a0b\"<\/span>,<\/span><\/code>\"conversations\"<\/span>: [<\/span><\/code>{<\/span><\/code>    \"from\"<\/span>: \"user\"<\/span>,<\/span><\/code>\"value\"<\/span>:\"\u4f60\u597d\uff0c\u4f60\u662f\uff1f\"<\/span><\/span><\/code>},<\/span><\/code>{<\/span><\/code>\"from\"<\/span>:\"assistant\"<\/span>,<\/span><\/code>\"value\"<\/span>:\"\u540c\u5b66\u4f60\u597d\uff0c\u6211\u8fd9\u8fb9\u662f\u7384\u59d0\u8c08AGI\uff0c\u60f3\u9080\u8bf7\u4f60\u4f53\u9a8c\u4e0b\u6211\u4eec\u7684AI\u5927\u6a21\u578b\u8bfe\u7a0b\u4ea7\u54c1\"<\/span><\/span><\/code>            },<\/span><\/code>            {<\/span><\/code>                \"from\"<\/span>: \"user\"<\/span>,<\/span><\/code>\"value\"<\/span>:\"\u62b1\u6b49\uff0c\u521a\u4e70\u4e86\u4e00\u4e2a\u5e74\u4f1a\u5458\uff0c\u8fd8\u6ca1\u5230\u671f\u3002\"<\/span><\/span><\/code>            },<\/span><\/code>            {<\/span><\/code>                \"from\"<\/span>: \"assistant\"<\/span>,<\/span><\/code>\"value\"<\/span>:\"\u4e0d\u597d\u610f\u601d\u540c\u5b66\uff0c\u6253\u6270\u5230\u60a8\u4e86\uff0c\u90a3\u665a\u4e9b\u65f6\u5019\u518d\u7ed9\u60a8\u6765\u7535\uff0c\u795d\u60a8\u5de5\u4f5c\u987a\u5229\uff0c\u518d\u89c1\"<\/span><\/span><\/code>            }<\/span><\/code>]<\/span><\/code>},<\/span><\/code>{<\/span><\/code>\"id\"<\/span>:\"p2\"<\/span>,<\/span><\/code>\"system\"<\/span>:\"\u4f60\u662f\u4e00\u4e2a\u9500\u552e\u5c0f\u52a9\u624b\uff0c\u4f60\u7684\u4efb\u52a1\u662f\u9080\u8bf7\u7a0b\u5e8f\u5458\u8d2d\u4e70AI\u5927\u6a21\u578b\u8bfe\u7a0b\"<\/span>,<\/span><\/code>        \"conversations\"<\/span>: [<\/span><\/code>... ...<\/span><\/code>        ]<\/span><\/code>    },<\/span><\/code>    ... ...<\/span><\/code>]<\/span><\/code><\/pre>\n<\/section>\n
\u63a5\u4e0b\u6765\u9700\u8981\u5b8c\u6210\u6570\u636e\u914d\u7f6e\uff0c\u5177\u4f53\u64cd\u4f5c\u5982\u4e0b\uff1a<\/p>\n
\u7b2c\u4e09\u6b65\u3001cd\u76ee\u5f55\uff0c\u627e\u5230 dataset_info.json<\/span><\/strong><\/p>\n
cd<\/span> LLaMa-Factory\/data\/dataset_info.json<\/span><\/span><\/span><\/p>\n
\u7b2c\u56db\u6b65\u3001\u5728\u5b57\u5178\u4e2d\u6dfb\u52a0\u81ea\u5df1\u7684\u6570\u636e\u4fe1\u606f<\/strong><\/p>\n
\n
<\/code>\"my_task\"<\/span>: {<\/span><\/code>\"file_name\"<\/span>:finetune_data.json#\u4f60\u81ea\u5df1\u6570\u636e\u7684\u8def\u5f84\uff0c\u8fd9\u91cc\u662f\u76f4\u63a5\u5b58\u653e\u5728LLaMa-Factory\/data\/finetune_data.json\u76ee\u5f55\u4e0b\u4e86\uff0c\u6240\u4ee5\u76f4\u63a5\u5199\u6570\u636e\u540d<\/span><\/code>\"formatting\"<\/span>: sharegpt<\/span><\/code>\"columns\"<\/span>: {<\/span><\/code>    \"messages\"<\/span>: \"conversations\"<\/span>,<\/span><\/code>\"tools\"<\/span>: \"id\"<\/span><\/span><\/code>},<\/span><\/code>\"tags\"<\/span>: {<\/span><\/code>\"role_tag\"<\/span>:\"from\"<\/span>,<\/span><\/code>\"content_tag\"<\/span>:\"value\"<\/span>,<\/span><\/code>\"user_tag\"<\/span>: \"user\"<\/span>,<\/span><\/code>\"assistant_tag\"<\/span>: \"assistant\"<\/span><\/span><\/code>}<\/span><\/code>}<\/span><\/code><\/code><\/pre>\n<\/section>\n
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\u2014<\/em>3<\/strong><\/em><\/span>\u2014<\/em><\/span><\/p>\n
\u5f00\u59cb\u5fae\u8c03\u8bad\u7ec3<\/span><\/strong><\/span><\/strong><\/p>\n
\u65b9\u6cd5\u4e00\u3001\u4ee5<\/span>LLaMA Board \u65b9\u5f0f\u8fdb\u884c\u9009\u53c2\u8bad\u7ec3<\/span><\/strong><\/span><\/strong><\/p>\n
\u8fd9\u79cd\u65b9\u5f0f\u524d\u7aef\u754c\u9762\u53cb\u597d\uff0c\u4f53\u9a8c\u611f\u597d\uff0c\u5177\u4f53\u8fd0\u884c\u5982\u4e0b\uff1a<\/span><\/section>\n
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli webui<\/span><\/span><\/section>\n
\u65b9\u5f0f\u4e8c\u3001\u7eaf\u540e\u53f0\u542f\u52a8\u65b9\u5f0f<\/span><\/strong><\/section>\n
\u5177\u4f53\u8fd0\u884c\u5982\u4e0b\uff1a<\/span><\/section>\n
export CUDA_VISIBLE_DEVICES=0,1,2,3<\/span><\/span><\/section>\n
llamafactory-cli train .\/sft_yaml\/my_finetune.yaml (\u542f\u52a8\u76ee\u5f55\u5728LLaMa-Factory\u4e0b<\/span>)<\/span><\/section>\n
<\/section>\n
my_finetune.yaml \u6587\u4ef6\u8bbe\u7f6e\u5982\u4e0b\uff1a<\/span><\/section>\n
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<\/code>### model<\/span><\/span><\/code>model_name_or_path: meta-llama\/Meta-Llama-3-8B-Instruct<\/span><\/span><\/code><\/code>### method<\/span><\/span><\/code>stage: sft<\/span><\/span><\/code>do_train: true<\/span><\/span><\/code>finetuning_type: full<\/span><\/span><\/code>deepspeed:examples\/deepspeed\/ds_z3_config.json(\u52a0\u901f\uff0c\u5f53\u663e\u5b58\u4e0d\u591f\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528ds_z3_offload_config.json)<\/span><\/span><\/code><\/code>### dataset<\/span><\/span><\/code>dataset: identity,alpaca_en_demo<\/span><\/span><\/code>template: llama3<\/span><\/span><\/code>cutoff_len: 1024 \uff08\u6700\u957f\u622a\u65ad\uff0c\u6570\u636e\u91cf\u5982\u679c\u5f88\u957f\uff0c\u8d85\u8fc7\u622a\u65ad\u7684\u90e8\u5206\u5c31\u65e0\u6cd5\u8bad\u7ec3\u5230\uff09<\/span><\/span><\/code>max_samples: 1000<\/span><\/span><\/code>overwrite_cache: true<\/span><\/span><\/code>preprocessing_num_workers: 16<\/span><\/span><\/code><\/code>### output<\/span><\/span><\/code>output_dir: saves\/llama3-8b\/full\/sft<\/span><\/span><\/code>logging_steps: 10<\/span><\/span><\/code>save_steps: 500<\/span><\/span><\/code>plot_loss: true<\/span><\/span><\/code>overwrite_output_dir: true<\/span><\/span><\/code><\/code>### train<\/span><\/span><\/code>per_device_train_batch_size: 1<\/span><\/span><\/code>gradient_accumulation_steps: 2<\/span><\/span><\/code>learning_rate: 1.0e-4<\/span><\/span><\/code>num_train_epochs: 3.0<\/span><\/span><\/code>lr_scheduler_type: cosine<\/span><\/span><\/code>warmup_ratio: 0.1<\/span><\/span><\/code>fp16: true<\/span><\/span><\/code>ddp_timeout: 180000000<\/span><\/span><\/code><\/code>### eval<\/span><\/span><\/code>val_size: 0.1<\/span><\/span><\/code>per_device_eval_batch_size: 1<\/span><\/span><\/code>eval_strategy: steps<\/span><\/span><\/code>eval_steps: 500<\/span><\/span><\/code><\/code><\/pre>\n<\/section>\n
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\u2014<\/em>4<\/strong><\/em><\/span>\u2014<\/em><\/span><\/p>\n
\u67e5\u770b\u8bad\u7ec3\u65e5\u5fd7<\/span><\/strong><\/p>\n
\u5fae\u8c03\u8fc7\u7a0b\u5b8c\u6bd5\u540e\uff0c\u4f60\u53ef\u524d\u5f80\u6307\u5b9a\u7684\u8f93\u51fa\u76ee\u5f55(output_dir)\uff0c\u5728\u5176\u4e2d\u8be6\u5c3d\u5ba1\u89c6\u8bad\u7ec3\u65e5\u5fd7\uff0c\u8fd9\u4e9b\u8bb0\u5f55\u6db5\u76d6\u4e86\u5173\u952e\u6307\u6807\uff0c\u6bd4\u5982\uff1a\u8bad\u7ec3\u635f\u5931\u4e0e\u9a8c\u8bc1\u635f\u5931\uff0c\u52a9\u4f60\u5168\u9762\u4e86\u89e3\u6a21\u578b\u5b66\u4e60\u7684\u8fdb\u5c55\u4e0e\u6027\u80fd\u3002<\/span><\/p>\n
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\u2014<\/em>5<\/strong><\/em><\/span>\u2014<\/em><\/span><\/p>\n
\u5fae\u8c03\u540e\u8bc4\u4f30<\/span><\/strong><\/p>\n
\u8981\u66f4\u65b0\u5927\u6a21\u578b\u7684\u9002\u914d\u5668\uff0c\u53ea\u9700\u7b80\u5355\u9009\u53d6\u76ee\u6807\u6a21\u578b\u5373\u53ef\u5b8c\u6210\u914d\u7f6e\u3002<\/span>\u800c\u7ecf\u8fc7\u5fae\u8c03\u8bad\u7ec3\u5f97\u5230\u7684\u5927\u6a21\u578b\uff0c\u5176\u90e8\u7f72\u548c\u8c03\u7528\u65b9\u5f0f\u5219\u4e0e\u5176\u4ed6\u9884\u8bad\u7ec3\u5927\u6a21\u578b\u4fdd\u6301\u4e00\u81f4\uff0c\u786e\u4fdd\u4e86\u4f7f\u7528\u7684\u4fbf\u6377\u6027\u548c\u901a\u7528\u6027\u3002<\/span><\/p>\n
\u53c2\u8003\u611f\u8c22\uff1ahttps:\/\/mp.weixin.qq.com\/s\/QHL_Q_3s6lfbqC4zkbQOKA<\/p>\n
\u4e3a\u4e86\u5e2e\u52a9\u540c\u5b66\u4eec\u5f7b\u5e95\u638c\u63e1 AI \u5927\u6a21\u578bAgent \u667a\u80fd\u4f53<\/span>\u3001\u77e5\u8bc6\u5e93\u3001\u5411\u91cf\u6570\u636e\u5e93\u3001 RAG\u3001\u5fae\u8c03\u79c1\u6709\u5927\u6a21\u578b<\/span>\u7684\u5e94\u7528\u5f00\u53d1\u3001\u90e8\u7f72\u3001\u751f\u4ea7\u5316<\/span>\uff0c<\/span>\u4eca\u5929\u76f4\u64ad<\/strong><\/span>\u548c\u540c\u5b66\u4eec\u6df1\u5ea6\u5256\u6790\uff0c\u8bf7\u540c\u5b66\u4eec\u70b9\u51fb\u4ee5\u4e0b<\/span>\u9884\u7ea6\u6309\u94ae\u514d\u8d39\u9884\u7ea6<\/strong><\/span>\u3002<\/span><\/p>\n
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\n
\u672c\u7bc7\u6587\u7ae0\u6765\u6e90\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7: \u7384\u59d0\u8c08AI<\/p><\/blockquote>\n<\/div>\n","raw":"","protected":false},"excerpt":{"rendered":"
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