[doc] fixed compatiblity with docusaurus

docs/source/en/Colossal-Auto/get_started/run_demo.md

@@ -9,7 +9,7 @@ Detailed instructions can be found in its `README.md`.
 
 ### 2. Integration with activation checkpoint
 
-Colossal-Auto's automatic search function for activation checkpointing finds the most efficient checkpoint within a given memory budget, rather than just aiming for maximum memory compression. To avoid a lengthy search process for an optimal activation checkpoint, Colossal-Auto has implemented a two-stage search process. This allows the system to find a feasible distributed training solution in a reasonable amount of time while still benefiting from activation checkpointing for memory management. The integration of activation checkpointing in Colossal-AI improves the efficiency and effectiveness of large model training. You can follow the [Resnet example](TBA).
+Colossal-Auto's automatic search function for activation checkpointing finds the most efficient checkpoint within a given memory budget, rather than just aiming for maximum memory compression. To avoid a lengthy search process for an optimal activation checkpoint, Colossal-Auto has implemented a two-stage search process. This allows the system to find a feasible distributed training solution in a reasonable amount of time while still benefiting from activation checkpointing for memory management. The integration of activation checkpointing in Colossal-AI improves the efficiency and effectiveness of large model training. You can follow the [Resnet example](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/auto_parallel).
 Detailed instructions can be found in its `README.md`.
 
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docs/source/zh/Colossal-Auto/get_started/run_demo.md → docs/source/zh-Hans/Colossal-Auto/get_started/run_demo.md

@@ -8,7 +8,7 @@ Colossal-Auto 可被用于为每一次操作寻找一个包含数据、张量（
 
 ### 2. 与 activation checkpoint 结合
 
-作为大模型训练中必不可少的显存压缩技术，Colossal-AI 也提供了对于 activation checkpoint 的自动搜索功能。相比于大部分将最大显存压缩作为目标的技术方案，Colossal-AI 的搜索目标是在显存预算以内，找到最快的 activation checkpoint 方案。同时，为了避免将 activation checkpoint 的搜索一起建模到 SPMD solver 中导致搜索时间爆炸，Colossal-AI 做了 2-stage search 的设计，因此可以在合理的时间内搜索到有效可行的分布式训练方案。 您可参考 [Resnet 示例](TBA)。
+作为大模型训练中必不可少的显存压缩技术，Colossal-AI 也提供了对于 activation checkpoint 的自动搜索功能。相比于大部分将最大显存压缩作为目标的技术方案，Colossal-AI 的搜索目标是在显存预算以内，找到最快的 activation checkpoint 方案。同时，为了避免将 activation checkpoint 的搜索一起建模到 SPMD solver 中导致搜索时间爆炸，Colossal-AI 做了 2-stage search 的设计，因此可以在合理的时间内搜索到有效可行的分布式训练方案。 您可参考 [Resnet 示例](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/auto_parallel)。
 详细的操作指引见其 `README.md`。
 
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