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Llama 3.1 \u5c06\u4e0a\u4e0b\u6587\u957f\u5ea6\u6269\u5c55\u5230\u4e86 128K\uff0c\u62e5\u6709 8B\u300170B \u548c 405B \u4e09\u4e2a\u7248\u672c\uff0c\u518d\u6b21\u4ee5\u4e00\u5df2\u4e4b\u529b\u62ac\u9ad8\u4e86\u5927\u6a21\u578b\u8d5b\u9053\u7684\u7ade\u4e89\u6807\u51c6\u3002<\/p>\n

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<\/a><\/p>\n

\u4e0e\u6b64\u540c\u65f6\uff0cMeta \u516c\u5e03\u4e86\u300aThe Llama 3 Herd of Models\u300b\u8bba\u6587\uff0c\u63ed\u793a\u4e86 Llama 3 \u7cfb\u5217\u6a21\u578b\u8fc4\u4eca\u4e3a\u6b62\u7684\u7814\u7a76\u7ec6\u8282\u3002<\/p>\n

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Llama3 \u8bba\u6587\u4eae\u70b9<\/strong><\/p>\n

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\u6b63\u5982 PyTorch \u521b\u59cb\u4eba\u3001Meta \u6770\u51fa\u5de5\u7a0b\u5e08 Soumith Chintala \u6240\u8bf4\uff0cLlama3 \u8bba\u6587\u63ed\u793a\u4e86\u8bb8\u591a\u5f88\u9177\u7684\u7ec6\u8282\uff0c\u5176\u4e2d\u4e4b\u4e00\u5c31\u662f\u57fa\u7840\u8bbe\u65bd\u7684\u6784\u5efa\u3002<\/p>\n

<\/a><\/p>\n

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<\/a><\/p>\n

<\/a><\/p>\n

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