From 7df4643c890527b65c4fa090ad7302eb961c1e3c Mon Sep 17 00:00:00 2001 From: Wenwen Qu Date: Tue, 26 Sep 2023 17:09:38 +0800 Subject: [PATCH] update mixed_precision.po --- .../locales/en/LC_MESSAGES/mixed_precision.po | 24 ++++++++++++++----- 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/doc/code-docs/locales/en/LC_MESSAGES/mixed_precision.po b/doc/code-docs/locales/en/LC_MESSAGES/mixed_precision.po index 33d6453..2520d1c 100644 --- a/doc/code-docs/locales/en/LC_MESSAGES/mixed_precision.po +++ b/doc/code-docs/locales/en/LC_MESSAGES/mixed_precision.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: InternLM \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-09-26 15:24+0800\n" +"POT-Creation-Date: 2023-09-26 17:04+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: en\n" @@ -25,11 +25,16 @@ msgstr "Mixed Precision" #: ../../source/mixed_precision.rst:3 msgid "" -"混合精度是指在模型训练的过程中同时使用16位和32位浮点数类型，是一种在最小化精度损失的前提下加速模型训练的方法。" +"混合精度是指在模型训练的过程中同时使用16位和32位浮点数类型，是一种在最小化精度损失的前提下加速模型训练的方法。 " "混合精度通过让模型的某些部分使用32位浮点数以保持数值稳定性，并在其余部分利用半精度浮点数加速训练并可以减少内存使用，在评估指标（如准确率）方面仍可以获得同等的训练效果。" msgstr "" -"Mixed precision refers to using both 16-bit and 32-bit floating-point types to train model, which can accelerate the model training while minimizing the accuracy loss. " -"Mixed precision training uses 32-bit floating-point types in certain parts of the model to maintain numerical stability, and accelerate training and reduce memory usage by using 16-bit floating-point types in other parts. Mixed precision can achieve the same training effect in evaluating indicators such as accuracy." +"Mixed precision refers to using both 16-bit and 32-bit floating-point " +"types to train model, which can accelerate the model training while " +"minimizing the accuracy loss. Mixed precision training uses 32-bit " +"floating-point types in certain parts of the model to maintain numerical " +"stability, and accelerate training and reduce memory usage by using " +"16-bit floating-point types in other parts. Mixed precision can achieve " +"the same training effect in evaluating indicators such as accuracy." #: internlm.core.naive_amp.NaiveAMPModel:1 of msgid "" @@ -62,11 +67,18 @@ msgstr "" #: ../../source/mixed_precision.rst:8 msgid "InternLM默认将模型转换为16位浮点数类型进行训练（在配置文件中可以设置默认类型为其他数据类型）。在使用混合精度时，需要在构建模型时使用" -msgstr "InternLM converts the model to 16-bit floating-point types for model training by default (the default type can be set to other data types in the configuration file). When using mixed precision, it is necessary to use " +msgstr "" +"InternLM converts the model to 16-bit floating-point types for model " +"training by default (the default type can be set to other data types in " +"the configuration file). When using mixed precision, it is necessary to " +"use " #: ../../source/mixed_precision.rst:14 msgid "将模型的某个子模块设置为32位浮点数类型进行训练，InternLM会在模型训练时自动将数据类型转换成需要的精度。" -msgstr "to set a sub-module of the model to 16-bit floating-point types for training, and InternLM will automatically convert the data type to the required precision during model training." +msgstr "" +"to set a sub-module of the model to 16-bit floating-point types for " +"training, and InternLM will automatically convert the data type to the " +"required precision during model training." #: ../../source/mixed_precision.rst:16 msgid "例如："