<\/div>\n<\/div>\n<\/div>\n<\/div>\n

7 \u6708 23 \u65e5\u661f\u671f\u4e8c\uff0cMeta \u5ba3\u5e03\u63a8\u51fa\u591a\u8bed\u8a00\u5927\u578b\u8bed\u8a00\u6a21\u578b (large language models\uff0c\u7b80\u79f0LLMs) Llama 3.1 \u7cfb\u5217\u3002Llama 3.1 \u5305\u542b\u7ecf\u8fc7\u9884\u8bad\u7ec3\u548c\u6307\u4ee4\u8c03\u6574\u7684\u6587\u672c\u8f93\u5165\/\u6587\u672c\u8f93\u51fa\u5f00\u6e90\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\u6a21\u578b\uff0c\u53c2\u6570\u5927\u5c0f\u4e3a 8B\u300170B \u548c\u9996\u6b21\u8fbe\u5230\u7684 405B\u3002<\/p>\n

\u7ecf\u8fc7\u6307\u4ee4\u8c03\u6574\u7684 Llama 3.1-405B \u662f\u76ee\u524d\u89c4\u6a21\u6700\u5927\u3001\u529f\u80fd\u6700\u5f3a\u5927\u7684\u5f00\u6e90\u8bed\u8a00\u6a21\u578b\uff0c\u53ef\u4e0e\u5e02\u573a\u4e0a\u6700\u597d\u7684\u4e13\u6709\u6a21\u578b\u76f8\u5ab2\u7f8e\uff0c7\u670823\u65e5\u5728 IBM\u00ae watsonx.ai\u2122 <\/a>\u4e0a\u63a8\u51fa\uff0c\u53ef\u90e8\u7f72\u5728 IBM \u4e91\u3001\u6df7\u5408\u4e91\u73af\u5883\u6216\u4f01\u4e1a\u5185\u90e8\u3002<\/p>\n

Llama 3.1 \u7248\u672c\u662f\u7ee7 4 \u6708 18 \u65e5\u63a8\u51fa Llama 3 \u578b\u53f7\u4e4b\u540e\u53d1\u5e03\u7684\u3002\u5728\u968f\u9644\u7684\u53d1\u5e03\u516c\u544a\u4e2d\uff0cMeta \u8868\u793a\u201c[\u4ed6\u4eec]\u8fd1\u671f\u7684\u76ee\u6807\u662f\u4f7f Llama 3 \u6210\u4e3a\u591a\u8bed\u8a00\u3001\u591a\u6a21\u6001\u3001\u5177\u6709\u66f4\u957f\u4e0a\u4e0b\u6587\u7684\u8f6f\u4ef6\uff0c\u5e76\u7ee7\u7eed\u63d0\u9ad8 LLM \u7684\u6574\u4f53\u6027\u80fd\uff0c\u5982\u63a8\u7406\u548c\u7f16\u7801\u80fd\u529b\u201d\u3002<\/p>\n

Llama 3.1 \u5927\u5e45\u589e\u52a0\u4e86\u4e0a\u4e0b\u6587\u957f\u5ea6\uff0c\u62e5\u6709\u6269\u5c55\u7684\u5de5\u5177\u548c\u591a\u8bed\u8a00\u529f\u80fd,\u5c55\u793a\u4e86\u5728\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u65b9\u9762\u53d6\u5f97\u7684\u91cd\u5927\u8fdb\u5c55\u3002<\/p>\n

\u5411\u65e0\u969c\u788d\u3001\u5f00\u653e\u3001\u8d1f\u8d23\u4efb\u7684\u4eba\u5de5\u667a\u80fd\u521b\u65b0\u8fc8\u51fa\u91cd\u8981\u4e00\u6b65<\/h2>\n
2023 \u5e74 12 \u6708\uff0c Meta \u548c IBM \u4e0e\u5168\u7403 50 \u591a\u4e2a\u521b\u59cb\u6210\u5458\u548c\u5408\u4f5c\u8005\u5171\u540c\u53d1\u8d77\u4e86\u4eba\u5de5\u667a\u80fd\u8054\u76df<\/a>\u3002\u4eba\u5de5\u667a\u80fd\u8054\u76df\u6c47\u805a\u4e86\u5de5\u4e1a\u754c\u3001\u521d\u521b\u4f01\u4e1a\u3001\u5b66\u672f\u754c\u3001\u7814\u7a76\u673a\u6784\u548c\u653f\u5e9c\u90e8\u95e8\u7684\u9886\u5148\u7ec4\u7ec7\uff0c\u65e8\u5728\u5851\u9020\u4eba\u5de5\u667a\u80fd\u7684\u53d1\u5c55\uff0c\u4ee5\u6700\u597d\u5730\u53cd\u6620\u6211\u4eec\u793e\u4f1a\u7684\u9700\u6c42\u548c\u590d\u6742\u6027\u3002\u81ea\u6210\u7acb\u4ee5\u6765\uff0c \u8054\u76df<\/a>\u5df2\u53d1\u5c55\u5230 100 \u591a\u4e2a\u6210\u5458\u3002<\/p>\n
\u66f4\u5177\u4f53\u5730\u8bf4\uff0c\u4eba\u5de5\u667a\u80fd\u8054\u76df\u81f4\u529b\u4e8e\u57f9\u517b\u4e00\u4e2a\u5f00\u653e\u7684\u793e\u533a\uff0c\u4f7f\u5f00\u53d1\u4eba\u5458\u548c\u7814\u7a76\u4eba\u5458\u80fd\u591f\u52a0\u5feb\u8d1f\u8d23\u4efb\u7684\u521b\u65b0\uff0c\u540c\u65f6\u786e\u4fdd\u4fe1\u4efb\u3001\u5b89\u5168\u3001\u4fdd\u969c\u3001\u591a\u6837\u6027\u3001\u79d1\u5b66\u4e25\u8c28\u6027\u548c\u7ecf\u6d4e\u7ade\u4e89\u529b\u3002\u4e3a\u6b64\uff0c\u8054\u76df\u652f\u6301\u5f00\u53d1\u548c\u90e8\u7f72\u57fa\u51c6\u4ee5\u53ca\u8bc4\u4f30\u6807\u51c6\u7684\u9879\u76ee\uff0c\u5e2e\u52a9\u5e94\u5bf9\u5168\u793e\u4f1a\u7684\u6311\u6218\uff0c\u652f\u6301\u5168\u7403\u4eba\u5de5\u667a\u80fd\u6280\u80fd\u5efa\u8bbe\uff0c\u9f13\u52b1\u4ee5\u5b89\u5168\u6709\u76ca\u7684\u65b9\u5f0f\u5f00\u653e\u5f00\u53d1\u4eba\u5de5\u667a\u80fd\u3002<\/p>\n
Llama 3.1 \u4e3a\u5168\u7403\u4eba\u5de5\u667a\u80fd\u793e\u533a\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5f00\u653e\u7684\u3001\u6700\u5148\u8fdb\u7684\u6a21\u578b\u65cf\u548c\u5f00\u53d1\u751f\u6001\u7cfb\u7edf\uff0c\u7528\u4e8e\u6784\u5efa\u3001\u5b9e\u9a8c\u548c\u8d1f\u8d23\u4efb\u5730\u6269\u5c55\u65b0\u521b\u610f\u548c\u65b0\u65b9\u6cd5\uff0c\u4ece\u800c\u63a8\u8fdb\u4e86\u8fd9\u4e00\u4f7f\u547d\u7684\u5b9e\u73b0\u3002\u9664\u4e86\u529f\u80fd\u5f3a\u5927\u7684\u65b0\u6a21\u578b\u5916\uff0c\u8be5\u7248\u672c\u8fd8\u5305\u62ec\u5f3a\u5927\u7684\u7cfb\u7edf\u7ea7\u5b89\u5168\u63aa\u65bd\u3001\u65b0\u7684\u7f51\u7edc\u5b89\u5168\u8bc4\u4f30\u63aa\u65bd\u548c\u66f4\u65b0\u7684\u63a8\u7406\u65f6\u95f4\u9632\u62a4\u63aa\u65bd\u3002\u603b\u4e4b\uff0c\u8fd9\u4e9b\u8d44\u6e90\u9f13\u52b1\u4e3a\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\u5f00\u53d1\u548c\u4f7f\u7528\u4fe1\u4efb\u4e0e\u5b89\u5168\u5de5\u5177\u7684\u6807\u51c6\u5316\u3002<\/p>\n

Llama 3.1-405B \u4e0e\u4e3b\u8981\u578b\u53f7\u7684\u6bd4\u8f83<\/h2>\n
\u5728\u56db\u6708\u4efd \u53d1\u5e03\u7684 Llama 3 <\/a>\u516c\u544a\u4e2d\uff0c\u8ba8\u8bba\u4e86\u5373\u5c06\u63a8\u51fa\u7684\u201c\u53c2\u6570\u8d85\u8fc7 400B\u201d\u7684 Llama \u578b\u53f7\uff0c\u5305\u62ec\u5bf9\u578b\u53f7\u6027\u80fd\u7684\u4e00\u4e9b\u521d\u6b65\u8bc4\u4f30\uff0c\u4f46\u76f4\u52307\u670823\u65e5\u7684\u53d1\u5e03\u4f1a\u624d\u516c\u5e03\u4e86\u5b83\u4eec\u7684\u5177\u4f53\u5c3a\u5bf8\u548c\u7ec6\u8282\u3002Llama 3.1 \u5bf9\u6240\u6709\u578b\u53f7\u90fd\u8fdb\u884c\u4e86\u91cd\u5927\u5347\u7ea7\uff0c\u65b0\u7684 405B \u5f00\u653e\u6e90\u7801\u578b\u53f7\u4e0e\u9886\u5148\u7684\u5c01\u95ed\u6e90\u4ee3\u7801\u4e13\u6709 LLM \u5b9e\u73b0\u4e86\u524d\u6240\u672a\u6709\u7684\u76f8\u5bf9\u4e00\u81f4\u3002<\/p>\n
7\u670823\u65e5\uff0cMeta \u516c\u53f8\u53d1\u5e03\u7684\u6700\u65b0\u6570\u636e\u5168\u9762\u5c55\u793a\u4e86 405B \u673a\u578b\u4e0e\u5176\u4ed6\u5148\u8fdb\u673a\u578b\u76f8\u6bd4\u6240\u53d6\u5f97\u7684\u9a84\u4eba\u6210\u7ee9\u3002\u4ee5\u4e0b\u662f\u5b83\u4e0e\u4e3b\u8981LLMs\u5728\u901a\u7528\u57fa\u51c6\u65b9\u9762\u7684\u6bd4\u8f83\u3002 [1]<\/a><\/p>\n
\n
\u672c\u79d1\u751f\u6c34\u5e73\u7684\u77e5\u8bc6\uff08MMLU\uff0c5 \u6837\u672c\uff09\uff08Massive Multitask Language Understanding\uff0c\u7b80\u79f0MMLU\uff0c\u5927\u89c4\u6a21\u591a\u4efb\u52a1\u8bed\u8a00\u7406\u89e3\uff09\uff1a<\/strong>\u7ecf\u8fc7\u6307\u4ee4\u8c03\u6574\u7684 Llama 405B \u5f97\u5206\u9ad8\u8fbe 87.3%\uff0c\u8d85\u8fc7\u4e86 OpenAI \u7684 GPT-4-Turbo \uff0886.5%\uff09\u3001Anthropic \u7684 Claude 3 Opus \uff0886.8%\uff09\u548c\u8c37\u6b4c\u7684 Gemini 1.5 Pro \uff0885.9%\uff09\uff0c\u540c\u65f6\u5b8c\u5168\u8d85\u8d8a\u4e86\u8c37\u6b4c\u6700\u5927\u7684 Gemini \u6a21\u578b Gemini 1.0 Ultra \uff0883.7%\uff09\u3002<\/li>\n
\u7814\u7a76\u751f\u6c34\u5e73\u63a8\u7406\uff08GPQA\uff0c0\u6837\u672c\uff09\uff08Grade School Physics Question Answering\uff0c\u7b80\u79f0 GPQA \uff0c\u7814\u7a76\u751f\u7ea7\u522b\u7684Google\u9a8c\u8bc1\u95ee\u7b54\u57fa\u51c6\u6d4b\u8bd5\uff09\uff1a<\/strong>Llama 405B Instruct \u7684 GPQA \u5f97\u5206\uff0850.7%\uff09\u4e0e Claude 3 Opus\uff0850.4%\uff09\u76f8\u5f53\uff0c\u8d85\u8fc7\u4e86 GPT-4T\uff0848.0%\uff09\uff0c\u5e76\u5927\u5927\u8d85\u8fc7\u4e86 Claude 3 Sonnet\uff0840.4%\uff09\u3001Claude 3 Haiku\uff0833.3%\uff09\u548c GPT-4\uff0835.7%\uff09\u3002<\/span><\/li>\n
\u6570\u5b66\u95ee\u9898\u89e3\u51b3\uff08MATH\uff0c0\u6837\u672c CoT\uff09\uff08 MATH \u6570\u636e\u96c6\u662f\u4e00\u4e2a\u4e13\u95e8\u8bbe\u8ba1\u6765\u8861\u91cf\u4eba\u5de5\u667a\u80fd\u89e3\u51b3\u6570\u5b66\u95ee\u9898\u80fd\u529b\u7684\u57fa\u51c6) (CoT, Chain of thought,\u601d\u7ef4\u94fe\uff0c\u80fd\u591f\u6fc0\u53d1LLM\u901a\u8fc7\u201c\u601d\u8003\u201d\u6765\u56de\u7b54\u4e00\u4e9b\u56f0\u96be\u95ee\u9898\u7684\u6280\u672f\uff09\uff1aLlama 405B Instruct\uff0873.8%\uff09\u4ec5\u6b21\u4e8e GPT-4o\uff0876.6%\uff09\u3002\u5b83\u8d85\u8d8a\u4e86 GPT-4T\uff0872.6%\uff09\u548c Anthropic \u7684\u6700\u65b0\u7248\u672c Claude 3.5 Sonnet\uff0871.1%\uff09\uff0c\u5e76\u660e\u663e\u4f18\u4e8e Claude 3 Opus\uff0860.1%\uff09\u3002\u5373\u4f7f\u5c06 Llama \u7684 0 \u6837\u672c MATH \u5206\u6570\u4e0e\u5176\u4ed6\u673a\u578b\u7684 4 \u6837\u672c MATH \u5206\u6570\u8fdb\u884c\u6bd4\u8f83\uff0cLlama \u7684\u8868\u73b0\u4e5f\u5927\u5927\u4f18\u4e8e GPT-4\uff0842.5%\uff09\u3001Gemini Ultra 1.0\uff0853.2%\uff09\u548c Gemini Pro 1.5\uff0858.5%\uff09\u3002<\/span><\/strong><\/li>\n
\u9605\u8bfb\u7406\u89e3\uff08DROP\uff0cF1\uff09\uff08Discrete Reasoning over Paragraphs\uff0c\u7b80\u79f0 DROP\uff0c\u9605\u8bfb\u7406\u89e3\u57fa\u51c6\u6d4b\u8bd5\uff09\uff1a<\/strong>\u57fa\u7840\u9884\u8bad\u7ec3\u7684 <\/span> Llama 405B\uff0884.8\uff09\u7684\u8868\u73b0\u4f18\u4e8e GPT-4o\uff0883.4\uff09\u3001Claude 3 Opus\uff0883.1\uff09\u3001Gemini 1.0 Ultra\uff0882.4\uff09\u548c Gemini 1.5 Pro\uff0878.9\uff09\u3002\u5176\u6027\u80fd\u4ec5\u6b21\u4e8e GPT-4T\uff0886.0\uff09\u548c Claude 3.5 Sonnet\uff0887.1\uff09\u3002<\/span><\/li>\n
\u77e5\u8bc6\u95ee\u7b54\uff08ARC-Challenge\uff0c25\u6837\u672c\uff09\uff08ARC-Challenge\uff0cAI Reasoning Challenge\uff0c\u4eba\u5de5\u667a\u80fd\u63a8\u7406\u6311\u6218\uff09\uff1a<\/strong>\u9884\u5148\u8bad\u7ec3\u7684 Llama 400B+\uff0896.1%\uff09\u4e0e GPT-4\uff0896.3%\uff09\u548c Claude 3 Opus\uff0896.4%\uff09\u7684\u6027\u80fd\u76f8\u5f53\u3002<\/span><\/li>\n
\u4ee3\u7801\uff08HumanEval\uff0c0\u6837\u672c\uff09\uff08humanEval\uff0c\u8bc4\u4f30\u5de5\u5177\uff0c\u7528\u6765\u8bc4\u4f30\u5728\u4ee3\u7801\u4e0a\u8bad\u7ec3\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u6027\u80fd\uff09\uff1a<\/strong>\u00a0\u7ecf\u8fc7\u6307\u5bfc\u8c03\u8bd5\u7684 Llama \u673a\u578b\uff0889.0%\uff09\u51e0\u4e4e\u662f\u540c\u7c7b\u4ea7\u54c1\u4e2d\u7684\u4f7c\u4f7c\u8005\uff0c\u9664 Claude 3.5 Sonnet \u548c GPT-4o \u5916\uff0c\u8f7b\u677e\u51fb\u8d25\u4e86\u5176\u4ed6\u6240\u6709\u673a\u578b\u3002<\/li>\n<\/ul>\n
\u900f\u8fc7\u6570\u5b57\u770b\u672c\u8d28<\/h3>\n
\u5728\u5c06 405B \u4e0e\u5176\u4ed6\u5148\u8fdb\u673a\u578b\u8fdb\u884c\u6bd4\u8f83\u65f6\uff0c\u6027\u80fd\u57fa\u51c6\u5e76\u4e0d\u662f\u552f\u4e00\u9700\u8981\u8003\u8651\u7684\u56e0\u7d20\u3002\u5176\u4ed6\u90e8\u5206\u6a21\u578b\u53ea\u80fd\u901a\u8fc7\u5e94\u7528\u7a0b\u5e8f\u63a5\u53e3\u8bbf\u95ee\uff0c\u4e14\u5e95\u5c42\u6a21\u578b\u53ef\u80fd\u4f1a\u5728\u672a\u77e5\u7684\u60c5\u51b5\u4e0b\u66f4\u6539\uff0c\u4e0e\u6b64\u4e0d\u540c\u7684\u662f\uff0cLlama 3.1-405B \u662f\u4e00\u4e2a\u7a33\u5b9a\u7684\u5e73\u53f0\uff0c\u53ef\u4ee5\u5728\u5176\u57fa\u7840\u4e0a\u6784\u5efa\u3001\u4fee\u6539\uff0c\u751a\u81f3\u5728\u4f01\u4e1a\u5185\u90e8\u8fd0\u884c\u3002\u8fd9\u79cd\u7a0b\u5ea6\u7684\u63a7\u5236\u548c\u53ef\u9884\u6d4b\u6027\u5bf9\u4e8e\u7814\u7a76\u4eba\u5458\u3001\u4f01\u4e1a\u548c\u5176\u4ed6\u91cd\u89c6\u4e00\u81f4\u6027\u548c\u53ef\u91cd\u590d\u6027\u7684\u5b9e\u4f53\u6765\u8bf4\u662f\u4e00\u5927\u798f\u97f3\u3002<\/p>\n
\u5982\u4f55\u66f4\u597d\u5730\u4f7f\u7528 Llama-3.1-405B<\/h2>\n
\u4e0e Meta \u4e00\u6837\uff0cIBM \u4e5f\u8ba4\u4e3a\u53ef\u884c\u7684\u5f00\u653e\u6a21\u578b\u6709\u52a9\u4e8e\u63d0\u4f9b\u66f4\u597d\u3001\u66f4\u5b89\u5168\u7684\u4ea7\u54c1\uff0c\u52a0\u901f\u521b\u65b0\uff0c\u5e76\u4fc3\u8fdb\u4eba\u5de5\u667a\u80fd\u5e02\u573a\u7684\u6574\u4f53\u5065\u5eb7\u53d1\u5c55\u3002\u590d\u6742\u7684 405B \u53c2\u6570\u5f00\u6e90\u6a21\u578b\u7684\u89c4\u6a21\u548c\u80fd\u529b\u4e3a\u5404\u79cd\u89c4\u6a21\u7684\u7ec4\u7ec7\u63d0\u4f9b\u4e86\u72ec\u7279\u7684\u673a\u4f1a\u548c\u7528\u4f8b\u3002<\/p>\n
\u9664\u4e86\u76f4\u63a5\u4f7f\u7528\u8be5\u6a21\u578b\u8fdb\u884c\u63a8\u7406\u548c\u6587\u672c\u751f\u6210\u5916\uff08\u9274\u4e8e\u5176\u5927\u5c0f\u548c\u76f8\u5e94\u7684\u8ba1\u7b97\u9700\u6c42\uff0c\u53ef\u80fd\u9700\u8981\u91cf\u5316\u6216\u5176\u4ed6\u4f18\u5316\u65b9\u6cd5\u624d\u80fd\u5728\u5927\u591a\u6570\u786c\u4ef6\u8bbe\u7f6e\u4e0a\u672c\u5730\u8fd0\u884c\uff09\uff0c405B \u8fd8\u53ef\u7528\u4e8e\u5176\u4ed6\u7528\u9014\uff1a<\/p>\n
\n
\u751f\u6210\u5408\u6210\u6570\u636e\uff1a<\/strong>\u5f53\u7528\u4e8e\u9884\u8bad\u7ec3\u3001\u5fae\u8c03\u6216\u6307\u4ee4\u8c03\u6574\u7684\u5408\u9002\u6570\u636e\u7a00\u7f3a\u6216\u8fc7\u4e8e\u6602\u8d35\u65f6\uff0c\u5408\u6210\u6570\u636e<\/a>\u53ef\u4ee5\u5f25\u8865\u8fd9\u4e00\u5dee\u8ddd\u3002405B \u53ef\u4ee5\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u4efb\u52a1\u548c\u7279\u5b9a\u9886\u57df\u5408\u6210\u6570\u636e\uff0c\u7528\u4e8e\u8bad\u7ec3\u53e6\u4e00\u4e2a LLM\u3002IBM \u7684 \u804a\u5929\u673a\u5668\u4eba\u7684\u5927\u89c4\u6a21\u5bf9\u9f50\uff08Large-scale Alignment for chatBots\uff0c\u7b80\u79f0LAB<\/a>) \u662f\u4e00\u79cd\u5206\u9636\u6bb5\u8bad\u7ec3\u534f\u8bae\uff0c\u7528\u4e8e\u5229\u7528\u5408\u6210\u6570\u636e\u6709\u6548\u66f4\u65b0 LLM\uff0c\u540c\u65f6\u4fdd\u7559\u6a21\u578b\u7684\u73b0\u6709\u77e5\u8bc6\u3002<\/li>\n
\u77e5\u8bc6\u63d0\u70bc\uff1a<\/strong>405B \u6a21\u578b\u7684\u77e5\u8bc6\u548c\u65b0\u5174\u80fd\u529b\u53ef\u4ee5\u63d0\u70bc\u5230\u4e00\u4e2a\u8f83\u5c0f\u7684\u6a21\u578b<\/a>\u4e2d\uff0c\u5c06\u5927\u578b \u201c\u6559\u5e08\u201d\u6a21\u578b\u7684\u80fd\u529b\u4e0e \u201c\u5b66\u751f\u201d\u6a21\u578b\uff08\u5982 8B \u6216 70B Llama 3.1\uff09\u5feb\u901f\u3001\u7ecf\u6d4e\u7684\u63a8\u7406\u80fd\u529b\u878d\u4e3a\u4e00\u4f53\u3002\u77e5\u8bc6\u7684\u63d0\u70bc\uff0c\u7279\u522b\u662f\u901a\u8fc7\u5bf9\u5927\u578b GPT \u6a21\u578b\u751f\u6210\u7684\u5408\u6210\u6570\u636e\u8fdb\u884c \u6307\u5bfc\u8c03\u6574<\/a>\uff0c\u5bf9\u4e8e\u521b\u5efa\u50cf Alpaca<\/a> \u548c Vicuna<\/a> \u8fd9\u6837\u6709\u5f71\u54cd\u529b\u7684\u57fa\u4e8e Llama \u7684\u6a21\u578b\u81f3\u5173\u91cd\u8981\u3002<\/li>\n
LLM \u62c5\u4efb\u6cd5\u5b98\uff1a<\/strong>\u7531\u4e8e\u4eba\u7c7b\u504f\u597d\u7684\u4e3b\u89c2\u6027\uff0c\u4ee5\u53ca\u73b0\u6709\u57fa\u51c6\u5bf9\u4eba\u7c7b\u504f\u597d\u7684\u8fd1\u4f3c\u80fd\u529b\u5e76\u4e0d\u5b8c\u5584\uff0c\u56e0\u6b64\u8bc4\u4f30 LLM \u53ef\u80fd\u975e\u5e38\u68d8\u624b\u3002\u4f8b\u5982\uff0c\u6b63\u5982 \u201cLlama 2\u201d<\/a> \u7814\u7a76\u8bba\u6587\u6240\u5c55\u793a\u7684\uff0c\u5927\u578b\u6a21\u578b\u53ef\u4ee5\u4f5c\u4e3a\u5176\u4ed6\u6a21\u578b\u54cd\u5e94\u8d28\u91cf\u7684\u516c\u6b63\u8bc4\u5224\u8005\u3002(\u6b32\u4e86\u89e3\u66f4\u591a\u6709\u5173 LLM \u62c5\u4efb\u6cd5\u5b98\u6280\u5de7\u7684\u529f\u6548\uff0c\u53ef\u4ee5\u4ece\u8fd9\u7bc7 2023 \u5e74\u7684\u8bba\u6587<\/a>\u5165\u624b\uff09\u3002<\/li>\n
\u529f\u80fd\u5f3a\u5927\u3001\u9488\u5bf9\u7279\u5b9a\u9886\u57df\u7684\u5fae\u8c03\uff1a<\/strong>\u8bb8\u591a\u9886\u5148\u7684\u5c01\u95ed\u5f0f\u6a21\u578b\u53ea\u5141\u8bb8\u6839\u636e\u5177\u4f53\u60c5\u51b5\u8fdb\u884c\u5fae\u8c03<\/a>\uff0c\u53ea\u80fd\u5bf9\u8f83\u65e7\u6216\u8f83\u5c0f\u7684\u6a21\u578b\u7248\u672c\u8fdb\u884c\u5fae\u8c03\uff0c\u6216\u8005\u6839\u672c\u4e0d\u5141\u8bb8\u8fdb\u884c\u5fae\u8c03\u3002\u76f8\u53cd\uff0cMeta \u5df2\u7ecf\u5c06 Llama 3.1-405B \u5b8c\u5168\u7528\u4e8e\u6301\u7eed\u7684\u9884\u8bad\u7ec3\uff08\u4ee5\u4fdd\u6301\u6a21\u578b\u7684\u5e38\u8bc6\u6700\u65b0\uff09\uff0c\u6216\u5bf9\u7279\u5b9a\u9886\u57df\u8fdb\u884c\u5fae\u8c03–watsonx Tuning Studio<\/a> \u5373\u5c06\u63a8\u51fa\u3002<\/li>\n<\/ul>\n
\u4e3a\u4e86\u6210\u529f\u63a8\u51fa Llama 3.1 \u6a21\u578b\uff0cMeta AI \u201c\u5f3a\u70c8\u5efa\u8bae\u201d \u4f7f\u7528\u50cf IBM\u00ae watsonx<\/a> \u8fd9\u6837\u63d0\u4f9b \u6a21\u578b\u8bc4\u4f30<\/a>\u3001 \u5b89\u5168\u9632\u62a4<\/a>\u548c \u68c0\u7d22\u589e\u5f3a\u751f\u6210 (retrieval augmented generation\uff0c\u7b80\u79f0 RAG)<\/a> \u7b49\u6838\u5fc3\u529f\u80fd\u7684\u5e73\u53f0\u3002<\/p>\n
\u9002\u7528\u4e8e\u6bcf\u79cd\u5c3a\u5bf8\u7684 llama 3.1 \u578b\u53f7\u7684\u5347\u7ea7\u7248<\/h2>\n
\u671f\u5f85\u5df2\u4e45\u7684 405B \u578b\u53f7\u53ef\u80fd\u662f Llama 3.1 \u7248\u672c\u4e2d\u6700<\/em>\u503c\u5f97\u5173\u6ce8\u7684\u5730\u65b9\uff0c\u4f46\u5b83\u8fdc\u4e0d\u662f\u552f\u4e00<\/em>\u503c\u5f97\u5173\u6ce8\u7684\u5730\u65b9\u3002\u867d\u7136 Llama 3.1 \u578b\u53f7\u4e0e Llama 3 \u91c7\u7528\u76f8\u540c\u7684\u5bc6\u96c6\u53d8\u538b\u5668\u67b6\u6784\uff0c\u4f46\u5728\u6240\u6709\u578b\u53f7\u4e2d\uff0c\u5b83\u4eec\u90fd\u5bf9 Llama 3 \u8fdb\u884c\u4e86\u591a\u9879\u91cd\u5927\u5347\u7ea7\u3002<\/p>\n
\u66f4\u957f\u7684\u4e0a\u4e0b\u6587\u7a97\u53e3<\/h3>\n\u5728\u6240\u6709\u7ecf\u8fc7\u9884\u8bad\u7ec3\u548c\u6307\u4ee4\u8c03\u6574\u7684 Llama 3.1 \u6a21\u578b\u4e2d\uff0c\u4e0a\u4e0b\u6587\u957f\u5ea6\u4ece Llama 3 \u4e2d\u7684 8,192 \u4e2a\u8bcd\u7ec4\u5927\u5e45\u589e\u52a0\u5230 Llama 3.1 \u4e2d\u7684 128,000 \u4e2a\u8bcd\u7ec4\uff0c\u589e\u5e45\u9ad8\u8fbe 1600%\u3002\u8fd9\u4f7f\u5f97 Llama 3.1 \u7684\u4e0a\u4e0b\u6587\u957f\u5ea6\u4e0e\u63d0\u4f9b\u7ed9\u4f01\u4e1a\u7528\u6237\u7684 GPT-4o \u7248\u672c\u76f8\u5f53\uff0c\u5927\u5927\u8d85\u8fc7 GPT-4\uff08\u6216 ChatGPT Free \u4e2d\u7684 GPT-4o \u7248\u672c\uff09\uff0c\u5e76\u4e0e Claude 3 \u63d0\u4f9b\u7684 20 \u4e07\u4e2a\u4ee4\u724c\u7a97\u53e3\u76f8\u5f53\u3002\u7531\u4e8e Llama 3.1 \u53ef\u4ee5\u90e8\u7f72\u5728\u7528\u6237\u9009\u62e9\u7684\u786c\u4ef6\u6216\u4e91\u63d0\u4f9b\u5546\u4e0a\uff0c\u56e0\u6b64\u5176\u4e0a\u4e0b\u6587\u957f\u5ea6 \u5728\u9700\u6c42\u91cf\u5927\u7684\u65f6\u671f\u4e0d\u4f1a\u53d7\u5230\u4e34\u65f6\u7f29\u51cf\u7684\u5f71\u54cd<\/a>\u3002\u540c\u6837\uff0cLlama 3.1 \u4e00\u822c\u4e5f\u4e0d\u53d7\u5e7f\u6cdb\u7684 \u4f7f\u7528\u9650\u5236<\/a>\u3002<\/p>\n
\u4e00\u4e2a\u6a21\u578b\u7684\u4e0a\u4e0b\u6587\u957f\u5ea6\uff08\u53c8\u79f0\u4e0a\u4e0b\u6587\u7a97\u53e3\uff09\u6307\u7684\u662f LLM \u5728\u4efb\u4f55\u7ed9\u5b9a\u65f6\u95f4\u5185\u53ef\u4ee5\u8003\u8651\u6216 \u201c\u8bb0\u5fc6\u201d \u7684\u6587\u672c\u603b\u91cf\uff08\u4ee5\u8bcd\u7ec4\u4e3a\u5355\u4f4d\uff09\u3002\u5f53\u5bf9\u8bdd\u3001\u6587\u6863\u6216\u4ee3\u7801\u5e93\u8d85\u8fc7\u6a21\u578b\u7684\u6700\u5927\u4e0a\u4e0b\u6587\u957f\u5ea6\u65f6\uff0c\u5fc5\u987b\u5bf9\u5176\u8fdb\u884c\u4fee\u526a\u6216\u6458\u8981\uff0c\u4ee5\u4fbf\u6a21\u578b\u7ee7\u7eed\u8fdb\u884c\u3002Llama 3.1 \u6269\u5c55\u4e86\u4e0a\u4e0b\u6587\u7a97\u53e3\uff0c\u8fd9\u610f\u5473\u7740 Llama \u6a21\u578b\u73b0\u5728\u53ef\u4ee5\u5728\u4e0d\u9057\u5fd8\u7ec6\u8282\u7684\u60c5\u51b5\u4e0b\u8fdb\u884c\u66f4\u957f\u65f6\u95f4\u7684\u5bf9\u8bdd\uff0c\u5e76\u5728\u8bad\u7ec3\u548c\u63a8\u7406\u8fc7\u7a0b\u4e2d\u6444\u5165\u66f4\u5927\u7684\u6587\u6863\u6216\u4ee3\u7801\u6837\u672c\u3002<\/p>\n
\u867d\u7136\u5c06 \u6587\u672c\u8f6c\u6362\u4e3atokens<\/a>\u4e0d\u9700\u8981\u4efb\u4f55\u56fa\u5b9a\u7684\u5355\u8bcd\u5230\u4ee3\u5e01\u7684 \u201c\u4ea4\u6362\u7387\u201d\uff0c\u4f46\u4e00\u4e2a\u5408\u9002\u7684\u4f30\u8ba1\u662f\u6bcf\u4e2a\u5355\u8bcd\u5927\u7ea6\u6709 1.5 \u4e2atoken\u3002\u56e0\u6b64\uff0cLlama 3.1 \u7684 128,000 \u4e2a\u6807\u8bb0\u4e0a\u4e0b\u6587\u7a97\u53e3\u76f8\u5f53\u4e8e\u7ea6 85,000 \u4e2a\u5355\u8bcd\u3002Hugging Face \u4e0a\u7684 Tokenizer Playground<\/a> \u662f\u67e5\u770b\u548c\u5b9e\u9a8c\u4e0d\u540c\u6a21\u578b\u5982\u4f55\u6807\u8bb0\u6587\u672c\u8f93\u5165\u7684\u7b80\u5355\u65b9\u6cd5\u3002<\/p>\n
Llama 3.1 \u6a21\u578b\u7ee7\u7eed\u4eab\u53d7\u4e3a Llama 3 \u63a8\u51fa\u7684\u65b0\u6807\u8bb0\u5316\u5668\u5e26\u6765\u7684\u597d\u5904\uff0c\u8be5\u6807\u8bb0\u5316\u5668\u5bf9\u8bed\u8a00\u7684\u7f16\u7801\u6548\u7387\u6bd4 Llama 2 \u9ad8\u5f97\u591a\u3002<\/p>\n
\u7ef4\u62a4\u5b89\u4fdd\u548c\u5b89\u5168<\/h3>\n
Meta \u516c\u53f8\u672c\u7740 \u5bf9\u521b\u65b0\u8d1f\u8d23\u4efb\u7684\u6001\u5ea6<\/a>\uff0c\u8c28\u614e\u800c\u5f7b\u5e95\u5730\u5904\u7406\u6269\u5927\u80cc\u666f\u957f\u5ea6\u7684\u95ee\u9898\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u4e4b\u524d\u7684\u5f00\u6e90\u5b9e\u9a8c\u5df2\u7ecf\u4ea7\u751f\u4e86 128,000 \u4e2a token \u7a97\u53e3<\/a>\uff0c\u751a\u81f3 100 \u4e07\u4e2a token \u7a97\u53e3<\/a>\u7684 Llama \u884d\u751f\u5de5\u5177\u3002\u5c3d\u7ba1\u8fd9\u4e9b\u9879\u76ee\u662f Meta \u81f4\u529b\u4e8e\u5f00\u653e\u6a21\u578b\u6240\u5e26\u6765\u7684\u597d\u5904\u7684\u6781\u4f73\u4f8b\u8bc1\uff0c\u4f46\u6211\u4eec\u4ecd\u5e94\u8c28\u614e\u5bf9\u5f85\uff1a \u6700\u8fd1\u7684\u7814\u7a76<\/a>\u8868\u660e\uff0c\u5982\u679c\u6ca1\u6709\u4e25\u683c\u7684\u5e94\u5bf9\u63aa\u65bd\uff0c\u8d85\u957f\u7684\u4e0a\u4e0b\u6587\u7a97\u53e3\u4f1a \u201c\u4e3a LLM \u63d0\u4f9b\u4e30\u5bcc\u7684\u65b0\u653b\u51fb\u9762\u201d\u3002<\/p>\n
\u5e78\u8fd0\u7684\u662f\uff0cLlama 3.1 \u7248\u672c\u8fd8\u5305\u542b\u4e86\u4e00\u5957\u65b0\u7684\u63a8\u7406\u5b88\u62a4\u8f68\u3002\u9664\u4e86 Llama Guard<\/a> \u548c CyberSec Eval<\/a> \u7684\u66f4\u65b0\u7248\u672c\u5916\uff0c\u8be5\u7248\u672c\u8fd8\u63a8\u51fa\u4e86 Prompt Guard\uff0c\u63d0\u4f9b\u76f4\u63a5\u548c\u95f4\u63a5\u63d0\u793a\u6ce8\u5165\u8fc7\u6ee4\u529f\u80fd\u3002Meta \u901a\u8fc7 CodeShield<\/a> \u8fdb\u4e00\u6b65\u964d\u4f4e\u98ce\u9669\uff0cCodeShield \u662f\u4e00\u79cd\u5f3a\u5927\u7684\u63a8\u7406\u65f6\u95f4\u8fc7\u6ee4\u5de5\u5177\uff0c\u65e8\u5728\u9632\u6b62\u5c06 LLMs \u751f\u6210\u7684\u4e0d\u5b89\u5168\u4ee3\u7801\u5f15\u5165\u751f\u4ea7\u7cfb\u7edf\u3002<\/p>\n
\u4e0e\u4efb\u4f55\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\u7684\u5b9e\u65bd\u4e00\u6837\uff0c\u660e\u667a\u7684\u505a\u6cd5\u662f\u53ea\u5728\u5177\u6709 \u5f3a\u5927\u5b89\u5168\u3001\u9690\u79c1\u548c\u5b89\u5168\u63aa\u65bd<\/a>\u7684\u5e73\u53f0\u4e0a\u90e8\u7f72\u6a21\u578b\u3002<\/p>\n
\u591a\u8bed\u79cd\u6a21\u578b<\/strong><\/h3>\n
\u7ecf\u8fc7\u9884\u8bad\u7ec3\u548c\u6307\u5bfc\u8c03\u6574\u7684\u5404\u79cd\u5927\u5c0f\u7684 Llama 3.1 \u6a21\u578b\u73b0\u5728\u90fd\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u8bed\u8a00\u3002\u9664\u82f1\u8bed\u5916\uff0cLlama 3.1 \u578b\u53f7\u8fd8\u80fd\u4f7f\u7528\u5176\u4ed6\u8bed\u8a00\uff0c\u5982\u897f\u73ed\u7259\u8bed\u3001\u8461\u8404\u7259\u8bed\u3001\u610f\u5927\u5229\u8bed\u3001\u5fb7\u8bed\u548c\u6cf0\u8bed\u3002Meta \u8fd8\u6307\u51fa\uff0c\u201c\u5176\u4ed6\u4e00\u4e9b\u8bed\u8a00\u201d \u4ecd\u5904\u4e8e\u8bad\u7ec3\u540e\u7684\u9a8c\u8bc1\u9636\u6bb5\uff0c\u672a\u6765\u53ef\u80fd\u4f1a\u53d1\u5e03\u3002<\/p>\n
\u4f18\u5316\u5de5\u5177\u4f7f\u7528<\/h3>\n
Llama 3.1 Instruct \u6a21\u578b\u9488\u5bf9 \u201c\u5de5\u5177\u4f7f\u7528\u201d \u8fdb\u884c\u4e86\u5fae\u8c03\uff0c\u8fd9\u610f\u5473\u7740 Meta \u4f18\u5316\u4e86\u8fd9\u4e9b\u6a21\u578b\u4e0e\u67d0\u4e9b\u7a0b\u5e8f\u7684\u63a5\u53e3\u80fd\u529b\uff0c\u4ee5\u8865\u5145\u6216\u6269\u5c55 LLM \u7684\u529f\u80fd\u3002\u8fd9\u5305\u62ec\u4e3a\u7279\u5b9a\u641c\u7d22\u3001\u56fe\u50cf\u751f\u6210\u3001\u4ee3\u7801\u6267\u884c\u548c\u6570\u5b66\u63a8\u7406\u5de5\u5177\u751f\u6210\u5de5\u5177\u8c03\u7528\u7684\u8bad\u7ec3\uff0c\u4ee5\u53ca\u5bf9\u96f6\u6837\u672c\u5de5\u5177\u4f7f\u7528\u7684\u652f\u6301\uff0c\u5373\u4e0e\u8bad\u7ec3\u4e2d\u4ee5\u524d\u672a\u89c1\u8fc7\u7684\u5de5\u5177\u987a\u5229\u96c6\u6210\u7684\u80fd\u529b\u3002<\/p>\n
\u5f00\u59cb\u4f7f\u7528 Llama 3.1<\/h2>\n
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\u5728 watsonx.ai\u2122 \u4e2d\u8bd5\u7528 Llama 3.1-405B<\/a><\/p>\n
\u7acb\u5373\u4f7f\u7528 Llama 3.1-405B \u548c watsonx.ai \u5f00\u59cb RAG \u6559\u7a0b\uff1a<\/p>\n
\n
\u65e0\u4ee3\u7801 RAG<\/a><\/li>\n
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\u5e26\u6709\u7f51\u7edc\u6570\u636e\u7684 RAG<\/a><\/li>\n<\/ul>\n
\n
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