diff --git a/docs/mindformers/docs/source_en/feature/parallel_training.md b/docs/mindformers/docs/source_en/feature/parallel_training.md
index f279c54adb3f82965db266fb7742ffa99125a407..14d19251edeaf7db64eb66d1b71b8cf0f1ee6f3e 100644
--- a/docs/mindformers/docs/source_en/feature/parallel_training.md
+++ b/docs/mindformers/docs/source_en/feature/parallel_training.md
@@ -157,7 +157,7 @@ For more information on configuring distributed parallel parameters, see the [Mi
 
 ## MindSpore Transformers Distributed Parallel Application Practices
 
-In the [Llama3-70B fine-tuning configuration](https://gitee.com/kong_de_shu/mindformers/blob/dev/research/llama3/llama3_70b/finetune_llama3_70b.yaml#) file provided on the official website, multiple distributed parallelism strategies are used to improve the training efficiency in the multi-node multi-device environment. The main parallelism strategies and key parameters involved in the configuration file are as follows:
+In the [Llama3-70B fine-tuning configuration](https://gitee.com/mindspore/mindformers/blob/dev/research/llama3/llama3_70b/finetune_llama3_70b.yaml#) file provided on the official website, multiple distributed parallelism strategies are used to improve the training efficiency in the multi-node multi-device environment. The main parallelism strategies and key parameters involved in the configuration file are as follows:
 
 - **Data parallelism**: No additional data parallelism is enabled (`data_parallel: 1`).
 - **Model parallelism**: A model is sliced into eight parts, which are computed on different devices (`model_parallel: 8`).
diff --git a/docs/mindformers/docs/source_zh_cn/feature/parallel_training.md b/docs/mindformers/docs/source_zh_cn/feature/parallel_training.md
index 96a9adcfdc741afba7ef4e6e1648e2728cddb467..0b8f4fd29a2fd403acbe769af617aab2584667f0 100644
--- a/docs/mindformers/docs/source_zh_cn/feature/parallel_training.md
+++ b/docs/mindformers/docs/source_zh_cn/feature/parallel_training.md
@@ -157,7 +157,7 @@ parallel_config:
 
 ## MindSpore Transformers 分布式并行应用实践
 
-在官网提供的[Llama3-70B微调配置](https://gitee.com/kong_de_shu/mindformers/blob/dev/research/llama3/llama3_70b/finetune_llama3_70b.yaml#)文件中，使用了多种分布式并行策略，以提升多机多卡环境中的训练效率。以下是该配置文件中涉及的主要并行策略和关键参数：
+在官网提供的[Llama3-70B微调配置](https://gitee.com/mindspore/mindformers/blob/dev/research/llama3/llama3_70b/finetune_llama3_70b.yaml#)文件中，使用了多种分布式并行策略，以提升多机多卡环境中的训练效率。以下是该配置文件中涉及的主要并行策略和关键参数：
 
 - **数据并行**：未启用额外的数据并行（`data_parallel: 1`）。
 - **模型并行**：模型被切分成8个部分，在不同设备上计算（`model_parallel: 8`）。
diff --git a/tools/ci_pipeline_gate_APIView/generate_pr_html.py b/tools/ci_pipeline_gate_APIView/generate_pr_html.py
index e0bd934483698bd1f78a138276cd3f254faae6f1..9c03e6fd0e2564bbcbed54c8ca072c9e34de9d04 100644
--- a/tools/ci_pipeline_gate_APIView/generate_pr_html.py
+++ b/tools/ci_pipeline_gate_APIView/generate_pr_html.py
@@ -1258,7 +1258,7 @@ if __name__ == "__main__":
     # 修改权限
     pythonlib_dir = os.path.dirname(os.path.dirname(sphinx.__file__))
     chmod_path = os.path.join(pythonlib_dir, 'sphinx')
-    cmd_chmod = ['sudo', 'chmod', '-R', '+w', chmod_path]
+    cmd_chmod = ['sudo', 'chmod', '-R', '777', chmod_path]
     subprocess.run(cmd_chmod)
 
     # 屏蔽sphinx 在python>=3.9时额外依赖引入的版本过高问题