diff --git a/doc/code-docs/locales/en/LC_MESSAGES/parallel.po b/doc/code-docs/locales/en/LC_MESSAGES/parallel.po
index 15a8d23..5e359c9 100644
--- a/doc/code-docs/locales/en/LC_MESSAGES/parallel.po
+++ b/doc/code-docs/locales/en/LC_MESSAGES/parallel.po
@@ -455,3 +455,51 @@ msgstr ""
 msgid "Whether the gradient is success updated, and the gradient."
 msgstr ""
 
+#: ../../source/parallel.rst:155
+msgid "混合精度"
+msgstr "Mixed Precision"
+
+#: ../../source/parallel.rst:156
+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."
+
+#: internlm.core.naive_amp.NaiveAMPModel:1 of
+msgid ""
+"This is a wrapper class for a model that automatically casts the model, "
+"its inputs, and outputs into fp16. It also provides options to cast the "
+"output back to fp32 and to synchronize buffers."
+msgstr ""
+
+#: internlm.core.naive_amp.NaiveAMPModel:4 of
+msgid "The model to be wrapped and cast into fp16."
+msgstr ""
+
+#: internlm.core.naive_amp.NaiveAMPModel:6 of
+msgid "If True, the output of this module is cast into fp32. Defaults to True."
+msgstr ""
+
+#: internlm.core.naive_amp.NaiveAMPModel:8 of
+msgid ""
+"The parallel group mode used in this module. Defaults to "
+"``ParallelMode.DATA``."
+msgstr ""
+
+#: internlm.core.naive_amp.NaiveAMPModel:11 of
+msgid "If True, the buffers are synchronized. Defaults to True."
+msgstr ""
+
+#: ../../source/parallel.rst:161
+msgid "InternLM默认将模型转换为16位精度进行训练（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 "
+
+#: ../../source/parallel.rst:167
+msgid "将模型的某个子模块设置为32精度进行训练，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."
+
+#: ../../source/parallel.rst:169
+msgid "例如："
+msgstr "For example:"
diff --git a/doc/code-docs/source/parallel.rst b/doc/code-docs/source/parallel.rst
index 8a1b942..8b6121e 100644
--- a/doc/code-docs/source/parallel.rst
+++ b/doc/code-docs/source/parallel.rst
@@ -154,7 +154,7 @@ ZeRO1.5 的实现使用了分层分片的概念，通过配置值 ``parallel.zer
 混合精度
 -----------------
 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。
+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。
 
 .. autoclass:: internlm.core.naive_amp.NaiveAMPModel
 
@@ -177,10 +177,10 @@ InternLM默认将模型转换为16位精度进行训练（在配置文件中可
             self.linear2 = nn.Linear(1, 4, bias=False)
 
     model = MlpModel()
-    # 将model.linear2设置为fp32模块
+    # set model.linear2 as fp32 module
     set_fp32_attr_to_module(model.linear2)
 
-    # 混合精度模型
+    # apply mixed precision
     model = NaiveAMPModel(
         model=model,
         output_to_fp32=True,