应用引擎

quot;name": "sts-qsbm5uor-4-u-0", "namespace": "bec-user-ns-rgzafffd", "resourceVersion": "48983205", "uid": "1d2977fb-168b-11ea

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25T09:28:13Z host: evs.bj.baidubce.com content-type: application/json authorization: bce-auth-v1/e8e4a9ced6794355a9a1b8a20b58d37b/2020-03-25T09:28:13Z/1800/content-type;host;x-date/4a1692dc4bab84f5801f79ea0c1fece3601cf73ecd94409d2a94b3942b971715

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25T09:28:13Z host: evs.bj.baidubce.com content-type: application/json authorization: bce-auth-v1/e8e4a9ced6794355a9a1b8a20b58d37b/2020-03-25T09:28:13Z/1800/content-type;host;x-date/4a1692dc4bab84f5801f79ea0c1fece3601cf73ecd94409d2a94b3942b971715

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根据这些方法是否将LLM的涌现能力（EA）提炼成小语言模型（SLM）来对这些方法进行分类。 因此，我们将这些方法分为两个不同的类别：标准 KD 和基于 EA 的 KD。 为了直观地表示，下图提供了LLM知识蒸馏的简要分类。 标准知识蒸馏 Standard KD旨在使学生模型学习LLM所拥有的常见知识，如输出分布和特征信息。这种方法类似于传统的KD，但区别在于教师模型是LLM。

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quot; }' 正确响应示例 HTTP/1.1 200 OK { "requestId": "dd734b63-6c12-4e40-af29-c472ef376054", "id": "32d372f7-f112-49e1-a9bc-2001ea15e1a4", "type": "

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25T09:28:13Z host: evs.bj.baidubce.com content-type: application/json authorization: bce-auth-v1/e8e4a9ced6794355a9a1b8a20b58d37b/2020-03-25T09:28:13Z/1800/content-type;host;x-date/4a1692dc4bab84f5801f79ea0c1fece3601cf73ecd94409d2a94b3942b971715

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agree-sender-15", "fromUuid": "6580c04b-fd52-4905-ba0a-467b260f4fa1", "fromName": "fab-con-742-auto-test", "toUuid": "bb696d38-8594-44da-aad9-6ea0fb37d56d

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Created Content-Type: application/json;charset=UTF-8 X-Bce-Request-Id: 8d51a788-e79d-4ced-a9e0-0662ec85b7f7 Server: BWS { "description": "", "id": "d3f6fd54fe3643459109ea0675c295a8&

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