diff --git a/docs/source/en/basics/booster_plugins.md b/docs/source/en/basics/booster_plugins.md
index 075b17a1b..57fa81343 100644
--- a/docs/source/en/basics/booster_plugins.md
+++ b/docs/source/en/basics/booster_plugins.md
@@ -19,26 +19,17 @@ We currently provide the following plugins:
 
 More plugins are coming soon.
 
+## Choosing Your Plugin
+
+Generally only one plugin is used to train a model. Our recommended use case for each plugin is as follows.
+
+- [Torch DDP Plugin](#torch-ddp-plugin): It is suitable for models with less than 2 billion parameters (e.g. Bert-3m, GPT2-1.5b).
+- [Torch FSDP Plugin](#torch-fsdp-plugin) / [Low Level Zero Plugin](#low-level-zero-plugin): It is suitable for models with less than 10 billion parameters (e.g. GPTJ-6b, MegatronLM-8b).
+- [Gemini Plugin](#gemini-plugin): It is suitable for models with more than 10 billion parameters (e.g. TuringNLG-17b) and is ideal for scenarios with **high cross-node bandwidth and medium to small-scale clusters (below a thousand cards)** (e.g. Llama2-70b).
+- [Hybrid Pararllel Plugin](#hybrid-parallel-plugin): It is suitable for models with more than 60 billion parameters, or special models such as those with exceptionally long sequences, very large vocabularies, and is best suited for scenarios with **low cross-node bandwidth and large-scale clusters (a thousand cards or more)** (e.g. GPT3-175b, Bloom-176b).
+
 ## Plugins
 
-### Torch DDP Plugin
-
-More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
-
-{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
-
-### Torch FSDP Plugin
-
-> ⚠ This plugin is not available when torch version is lower than 1.12.0.
-
-> ⚠ This plugin does not support save/load sharded model checkpoint now.
-
-> ⚠ This plugin does not support optimizer that use multi params group.
-
-More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html).
-
-{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
-
 ### Low Level Zero Plugin
 
 This plugin implements Zero-1 and Zero-2 (w/wo CPU offload), using `reduce` and `gather` to synchronize gradients and weights.
@@ -87,13 +78,22 @@ This plugin implements the combination of various parallel training strategies a
 
 {{ autodoc:colossalai.booster.plugin.HybridParallelPlugin }}
 
-## Choosing Your Plugin
+### Torch DDP Plugin
 
-Generally only one plugin is used to train a model. Our recommended use case for each plugin is as follows.
+More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
 
-- [Torch DDP Plugin](#torch-ddp-plugin): It is suitable for models with less than 2 billion parameters (e.g. Bert-3m, GPT2-1.5b).
-- [Torch FSDP Plugin](#torch-fsdp-plugin) / [Low Level Zero Plugin](#low-level-zero-plugin): It is suitable for models with less than 10 billion parameters (e.g. GPTJ-6b, MegatronLM-8b).
-- [Gemini Plugin](#gemini-plugin): It is suitable for models with more than 10 billion parameters (e.g. TuringNLG-17b) and is ideal for scenarios with **high cross-node bandwidth and medium to small-scale clusters (below a thousand cards)** (e.g. Llama2-70b).
-- [Hybrid Pararllel Plugin](#hybrid-parallel-plugin): It is suitable for models with more than 60 billion parameters, or special models such as those with exceptionally long sequences, very large vocabularies, and is best suited for scenarios with **low cross-node bandwidth and large-scale clusters (a thousand cards or more)** (e.g. GPT3-175b, Bloom-176b).
+{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
+
+### Torch FSDP Plugin
+
+> ⚠ This plugin is not available when torch version is lower than 1.12.0.
+
+> ⚠ This plugin does not support save/load sharded model checkpoint now.
+
+> ⚠ This plugin does not support optimizer that use multi params group.
+
+More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html).
+
+{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
 
 <!-- doc-test-command: echo  -->
diff --git a/docs/source/zh-Hans/basics/booster_plugins.md b/docs/source/zh-Hans/basics/booster_plugins.md
index 0857f44e1..d4ef7012f 100644
--- a/docs/source/zh-Hans/basics/booster_plugins.md
+++ b/docs/source/zh-Hans/basics/booster_plugins.md
@@ -1,6 +1,7 @@
 # Booster 插件
 
-作者: [Hongxin Liu](https://github.com/ver217), [Baizhou Zhang](https://github.com/Fridge003)
+作者: [Hongxin Liu](https://github.com/ver217), [Baizhou Zhang](https://github.com/Fridge003), [Pengtai Xu](https://github.com/ppt0011)
+
 
 **前置教程:**
 - [Booster API](./booster_api.md)
@@ -19,27 +20,14 @@
 
 更多插件即将推出。
 
+## 插件选择
+- [Torch DDP 插件](#torch-ddp-插件): 适用于参数少于 20 亿的模型（例如 Bert-3m、GPT2-1.5b）。
+- [Torch FSDP 插件](#torch-fsdp-插件) / [Low Level Zero 插件](#low-level-zero-插件): 适用于参数少于 100 亿的模型（例如 GPTJ-6b、MegatronLM-8b）。
+- [Gemini 插件](#gemini-插件): 适合参数超过 100 亿的模型（例如 TuringNLG-17b），且**跨节点带宽高、中小规模集群（千卡以下）**的场景（例如 Llama2-70b）。
+- [Hybrid Pararllel 插件](#hybrid-parallel-插件): 适合参数超过 600 亿的模型、超长序列、超大词表等特殊模型，且**跨节点带宽低、大规模集群（千卡以上）**的场景（例如 GPT3-175b、Bloom-176b）。
+
 ## 插件
 
-### Torch DDP 插件
-
-更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
-
-{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
-
-### Torch FSDP 插件
-
-> ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。
-
-> ⚠ 该插件现在还不支持保存/加载分片的模型 checkpoint。
-
-> ⚠ 该插件现在还不支持使用了multi params group的optimizer。
-
-更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html).
-
-{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
-
-
 ### Low Level Zero 插件
 
 该插件实现了 Zero-1 和 Zero-2（使用/不使用 CPU 卸载），使用`reduce`和`gather`来同步梯度和权重。
@@ -87,10 +75,22 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累
 
 {{ autodoc:colossalai.booster.plugin.HybridParallelPlugin }}
 
-## 插件选择
-- [Torch DDP 插件](#torch-ddp-插件): 适用于参数少于 20 亿的模型（例如 Bert-3m、GPT2-1.5b）。
-- [Torch FSDP 插件](#torch-fsdp-插件) / [Low Level Zero 插件](#low-level-zero-插件): 适用于参数少于 100 亿的模型（例如 GPTJ-6b、MegatronLM-8b）。
-- [Gemini 插件](#gemini-插件): 适合参数超过 100 亿的模型（例如 TuringNLG-17b），且**跨节点带宽高、中小规模集群（千卡以下）**的场景（例如 Llama2-70b）。
-- [Hybrid Pararllel 插件](#hybrid-parallel-插件): 适合参数超过 600 亿的模型、超长序列、超大词表等特殊模型，且**跨节点带宽低、大规模集群（千卡以上）**的场景（例如 GPT3-175b、Bloom-176b）。
+### Torch DDP 插件
+
+更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
+
+{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
+
+### Torch FSDP 插件
+
+> ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。
+
+> ⚠ 该插件现在还不支持保存/加载分片的模型 checkpoint。
+
+> ⚠ 该插件现在还不支持使用了multi params group的optimizer。
+
+更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html).
+
+{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}
 
 <!-- doc-test-command: echo  -->