diff --git a/docs/faq/model_faq.md b/docs/faq/model_faq.md
index 11e0b6f689ff4493c4981a72f06a1a5bd4620d6d..eac755dbe87e2629fa28da56730f239d76128264 100644
--- a/docs/faq/model_faq.md
+++ b/docs/faq/model_faq.md
@@ -219,27 +219,27 @@ cd gmp-6.1.0
 make -j128
 make install
 cd ../
-wget https://ftp.swin.edu.au/gnu/mpfr/mpfr-4.1.1.tar.gz --no-check-certificate
+wget https://ftp.swin.edu.au/gnu/mpfr/mpfr-4.1.1.tar.gz
 tar -zxvf mpfr-4.1.1.tar.gz
 cd mpfr-4.1.1
 ./configure --prefix=/usr/local/mpfr --with-gmp=/usr/local/gmp # 该步骤若报错，替换命令为：./configure --with-gmp=/usr/local/gmp
 make -j128
 make install
 cd ../
-wget https://github.com/OpenMathLib/OpenBLAS/archive/refs/tags/v0.3.24.zip --no-check-certificate
+wget https://github.com/OpenMathLib/OpenBLAS/archive/refs/tags/v0.3.24.zip
 unzip v0.3.24.zip
 cd OpenBLAS-0.3.24
 make -j128
 make PREFIX=/usr/local install
 cd ../
-wget https://github.com/sqlite/sqlite/archive/refs/tags/version-3.36.0.tar.gz --no-check-certificate
+wget https://github.com/sqlite/sqlite/archive/refs/tags/version-3.36.0.tar.gz
 tar -xzvf version-3.36.0.tar.gz
 cd sqlite-version-3.36.0
 CFLAGS="-DSQLITE_ENABLE_COLUMN_METADATA=1" ./configure
 make -j128
 make install
 cd ../
-wget https://github.com/OSGeo/PROJ/archive/refs/tags/7.2.0.tar.gz --no-check-certificate
+wget https://github.com/OSGeo/PROJ/archive/refs/tags/7.2.0.tar.gz
 tar -xzvf 7.2.0.tar.gz
 cd PROJ-7.2.0
 mkdir build
@@ -405,7 +405,7 @@ export LD_PRELOAD={conda_env_path}/site-packages/sklearn/__check_build/../../sci
 #### 7.1 模型所需的预训练权重文件因网络问题下载失败如何解决？
 预训练权重文件下载失败，可以根据报错链接，手动下载，拷贝到用户名对应目录：
    ```
-   wget ckpt_file --no-check-certificate # 将预训练权重文件的链接记为ckpt_file
+   wget ckpt_file # 将预训练权重文件的链接记为ckpt_file
    cp ckpt_file {root}/.cache/torch/hub/checkpoints/resnet-*.pth # 将用户根目录记为{root}
    ```
 
diff --git a/model_examples/Diffusion-Planner/README.md b/model_examples/Diffusion-Planner/README.md
index 6fe5ea84664897479756d3b1107f59cafa0ab94d..19ff5b507c8ac0eeb6a149ec00905afcfd6fcc6e 100644
--- a/model_examples/Diffusion-Planner/README.md
+++ b/model_examples/Diffusion-Planner/README.md
@@ -195,7 +195,7 @@ make install
 ```
 安装mpfr的命令
 ```
-wget https://ftp.swin.edu.au/gnu/mpfr/mpfr-4.1.1.tar.gz --no-check-certificate
+wget https://ftp.swin.edu.au/gnu/mpfr/mpfr-4.1.1.tar.gz
 tar -zxvf mpfr-4.1.1.tar.gz
 cd mpfr-4.1.1
 ./configure --prefix=/usr/local/mpfr --with-gmp=/usr/local/gmp
@@ -207,7 +207,7 @@ make install
 ```
 安装OpenBLAS
 ```
-wget https://github.com/OpenMathLib/OpenBLAS/archive/refs/tags/v0.3.24.zip --no-check-certificate
+wget https://github.com/OpenMathLib/OpenBLAS/archive/refs/tags/v0.3.24.zip
 unzip v0.3.24.zip
 cd OpenBLAS-0.3.24
 make -j8
diff --git a/model_examples/GameFormer/README.md b/model_examples/GameFormer/README.md
index 52071d28980e982600bd709f2a1b5b188f428fe5..04cc0b3993285a86e75a0cf3471f5efd35abe68f 100644
--- a/model_examples/GameFormer/README.md
+++ b/model_examples/GameFormer/README.md
@@ -89,7 +89,7 @@
     ```
     对于arm64架构Linux系统，waymo官方并没有提供预先编译好whl包。为了方便用户使用，我们提供arm64系统编译的whl包，可以直接在华为云OBS上进行下载。
     ```
-    wget --no-check-certificate https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/DrivingSDK/packages/waymo_open_dataset_tf_2.11.0-1.5.0-py3-none-any.whl
+    wget https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/DrivingSDK/packages/waymo_open_dataset_tf_2.11.0-1.5.0-py3-none-any.whl
     pip install -r requirements_waymo.txt
     pip install waymo_open_dataset_tf_2.11.0-1.5.0-py3-none-any.whl
     ```
diff --git a/model_examples/HPTR/README.md b/model_examples/HPTR/README.md
index cf7858ed82941aab613575473a037f1ca6b83df9..9fabab94fdfff223606e32796ce0e9236bcbd7ee 100644
--- a/model_examples/HPTR/README.md
+++ b/model_examples/HPTR/README.md
@@ -96,7 +96,7 @@ HPTR 是一种用于自动驾驶实时运动预测的层次化Transformer模型
     ```
     对于 arm64 架构 Linux 系统，waymo 官方并没有提供预先编译好 whl 包。为了方便用户使用，我们提供 arm64 系统编译的 whl 包，可以直接在华为云 OBS 上进行下载：
     ```
-    wget --no-check-certificate https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/DrivingSDK/packages/waymo_open_dataset_tf_2.11.0-1.5.0-py3-none-any.whl
+    wget https://pytorch-package.obs.cn-north-4.myhuaweicloud.com/DrivingSDK/packages/waymo_open_dataset_tf_2.11.0-1.5.0-py3-none-any.whl
     pip install waymo_open_dataset_tf_2.11.0-1.5.0-py3-none-any.whl
     ```
 
diff --git a/model_examples/LaneSegNet/README.md b/model_examples/LaneSegNet/README.md
index f24d1f95b6e479a6bec386d49674b20528c18e5a..d37c94ebc9642a1ee1761ca1ce238df4b1d145f5 100644
--- a/model_examples/LaneSegNet/README.md
+++ b/model_examples/LaneSegNet/README.md
@@ -143,9 +143,8 @@ code_path=DrivingSDK/model_examples/LaneSegNet
 
   - 安装毕昇编译器
 
-  将CANN包安装目录记为cann_root_dir，执行下列命令安装毕昇编译器。
+  将CANN包安装目录记为cann_root_dir，执行下列命令安装毕昇编译器， 在官网下载毕昇编译器4.1.0版本：https://www.hikunpeng.com/zh/developer/devkit/download/bishengcompiler 。
   ```
-  wget https://kunpeng-repo.obs.cn-north-4.myhuaweicloud.com/BiSheng%20Enterprise/BiSheng%20Enterprise%20203.0.0/BiShengCompiler-4.1.0-aarch64-linux.tar.gz
   tar -xvf BiShengCompiler-4.1.0-aarch64-linux.tar.gz
   export PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/bin:$PATH
   export LD_LIBRARY_PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/lib:$LD_LIBRARY_PATH
@@ -154,7 +153,7 @@ code_path=DrivingSDK/model_examples/LaneSegNet
 
   - 安装依赖，将安装mpdecimal依赖包的目录记为mpdecimal_install_path。
   ```
-  wget --no-check-certificate https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
+  wget https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
   tar -xvf mpdecimal-2.5.1.tar.gz
   cd mpdecimal-2.5.1
   bash ./configure --prefix=mpdecimal_install_path
diff --git a/model_examples/MapTR/README.md b/model_examples/MapTR/README.md
index 761bc48370b9498c76a575fab91f2ecf7cede152..9254871f46c7bd36dcd456aa9a65a2c765c91963 100644
--- a/model_examples/MapTR/README.md
+++ b/model_examples/MapTR/README.md
@@ -158,9 +158,8 @@ MapTR是一种高效的端到端Transformer模型，用于在线构建矢量化
 
   - 安装毕昇编译器
 
-  将CANN包安装目录记为cann_root_dir，执行下列命令安装毕昇编译器。
+  将CANN包安装目录记为cann_root_dir，执行下列命令安装毕昇编译器，官网下载毕昇编译器4.1.0版本：https://www.hikunpeng.com/zh/developer/devkit/download/bishengcompiler。
   ```
-  wget https://kunpeng-repo.obs.cn-north-4.myhuaweicloud.com/BiSheng%20Enterprise/BiSheng%20Enterprise%20203.0.0/BiShengCompiler-4.1.0-aarch64-linux.tar.gz
   tar -xvf BiShengCompiler-4.1.0-aarch64-linux.tar.gz
   export PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/bin:$PATH
   export LD_LIBRARY_PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/lib:$LD_LIBRARY_PATH
@@ -169,7 +168,7 @@ MapTR是一种高效的端到端Transformer模型，用于在线构建矢量化
 
   - 安装依赖，将安装mpdecimal依赖包的目录记为mpdecimal_install_path。
   ```
-  wget --no-check-certificate https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
+  wget https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
   tar -xvf mpdecimal-2.5.1.tar.gz
   cd mpdecimal-2.5.1
   bash ./configure --prefix=mpdecimal_install_path
diff --git a/model_examples/OpenVLA/README.md b/model_examples/OpenVLA/README.md
index ade23a007992b92494512cfd23ee3c197e3258f1..09f54bbbb1f6b844d81f3cc737cc62811962a17b 100644
--- a/model_examples/OpenVLA/README.md
+++ b/model_examples/OpenVLA/README.md
@@ -128,7 +128,7 @@ OpenVLA 是一个 70 亿参数的开源视觉 - 语言 - 动作模型，基于 O
 ```shell
 mkdir gperftools
 cd gperftools
-wget https://github.com/gperftools/gperftools/releases/download/gperftools-2.16/gperftools-2.16.tar.gz --no-check-certificate
+wget https://github.com/gperftools/gperftools/releases/download/gperftools-2.16/gperftools-2.16.tar.gz
 tar -zvxf gperftools-2.16.tar.gz
 cd gperftools-2.16
 ./configure --prefix=/usr/local/lib --with-tcmalloc-pagesize=64
diff --git a/model_examples/PivotNet/README.md b/model_examples/PivotNet/README.md
index 2d624f3926f07fe82b38b5bfaf9da81410bd4786..4ab92a449c466606aa0803230428e19c8039987b 100644
--- a/model_examples/PivotNet/README.md
+++ b/model_examples/PivotNet/README.md
@@ -154,9 +154,8 @@ code_path=model_examples/PivotNet
 
   - 安装毕昇编译器
 
-  将CANN包安装目录记为cann_root_dir，执行下列命令安装毕昇编译器。
+  将CANN包安装目录记为cann_root_dir，执行下列命令安装毕昇编译器，官网下载毕昇编译器4.1.0版本：https://www.hikunpeng.com/zh/developer/devkit/download/bishengcompiler。
   ```
-  wget https://kunpeng-repo.obs.cn-north-4.myhuaweicloud.com/BiSheng%20Enterprise/BiSheng%20Enterprise%20203.0.0/BiShengCompiler-4.1.0-aarch64-linux.tar.gz
   tar -xvf BiShengCompiler-4.1.0-aarch64-linux.tar.gz
   export PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/bin:$PATH
   export LD_LIBRARY_PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/lib:$LD_LIBRARY_PATH
@@ -165,7 +164,7 @@ code_path=model_examples/PivotNet
 
   - 安装依赖，将安装mpdecimal依赖包的目录记为mpdecimal_install_path。
   ```
-  wget --no-check-certificate https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
+  wget https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
   tar -xvf mpdecimal-2.5.1.tar.gz
   cd mpdecimal-2.5.1
   bash ./configure --prefix=mpdecimal_install_path
@@ -232,9 +231,9 @@ code_path=model_examples/PivotNet
 ```
 cd /path/to/pivotnet
 cd assets/weights
-wget --no-check-certificate https://github.com/wenjie710/PivotNet/releases/download/v1.0/efficientnet-b0-355c32eb.pth .
-wget --no-check-certificate https://github.com/wenjie710/PivotNet/releases/download/v1.0/resnet50-0676ba61.pth .
-wget --no-check-certificate https://github.com/wenjie710/PivotNet/releases/download/v1.0/upernet_swin_tiny_patch4_window7_512x512.pth .
+wget https://github.com/wenjie710/PivotNet/releases/download/v1.0/efficientnet-b0-355c32eb.pth .
+wget https://github.com/wenjie710/PivotNet/releases/download/v1.0/resnet50-0676ba61.pth .
+wget https://github.com/wenjie710/PivotNet/releases/download/v1.0/upernet_swin_tiny_patch4_window7_512x512.pth .
 ```
 
 - 生成模型训练数据
diff --git a/model_examples/QCNet/README.md b/model_examples/QCNet/README.md
index 41dd8763298b2a643455b90722b5c061703bc8fa..4c34c10cc6cae3ea69c3a1fb4f6485905c0c13d7 100644
--- a/model_examples/QCNet/README.md
+++ b/model_examples/QCNet/README.md
@@ -125,7 +125,7 @@ code_path=model_examples/QCNet
     安装tcmalloc（适用OS: __openEuler__）
     ```shell
     mkdir gperftools && cd gperftools
-    wget --no-check-certificate https://github.com/gperftools/gperftools/releases/download/gperftools-2.16/gperftools-2.16.tar.gz
+    wget https://github.com/gperftools/gperftools/releases/download/gperftools-2.16/gperftools-2.16.tar.gz
     tar -zvxf gperftools-2.16.tar.gz
     cd gperftools-2.16
     ./configure
diff --git a/model_examples/Sparse4D/README.md b/model_examples/Sparse4D/README.md
index 7f35f44d4d2eee1867bea0b2fee4aba2273991fc..c9aaa630dab2f94fe4a1ab4cc4c9f68387d9b574 100644
--- a/model_examples/Sparse4D/README.md
+++ b/model_examples/Sparse4D/README.md
@@ -153,7 +153,7 @@
 ```
 mkdir gperftools
 cd gperftools
-wget https://github.com/gperftools/gperftools/releases/download/gperftools-2.16/gperftools-2.16.tar.gz --no-check-certificate
+wget https://github.com/gperftools/gperftools/releases/download/gperftools-2.16/gperftools-2.16.tar.gz
 tar -zvxf gperftools-2.16.tar.gz
 cd gperftools-2.16
 ./configure --prefix=/usr/local/lib --with-tcmalloc-pagesize=64
diff --git a/model_examples/SparseDrive/README.md b/model_examples/SparseDrive/README.md
index e11a17431a4dc8bf72cf4e6c79f3b2783e38bdb1..91ee9e1afc037d168cce0fc42b21fa141c714d45 100644
--- a/model_examples/SparseDrive/README.md
+++ b/model_examples/SparseDrive/README.md
@@ -171,7 +171,7 @@ SparseDrive是一种基于稀疏化表征的端到端自动驾驶模型，基于
   (option) bash test/train_8p_performance.sh --batch_node_size_stage1=64 --batch_node_size_stage2=48 --num_npu=8
 
   # (option)若没有进行全量精度训练，可使用如下命令下载stage1的权重以测试stage2的性能
-  wget https://github.com/swc-17/SparseDrive/releases/download/v1.0/sparsedrive_stage1.pth --no-check-certificate -O ckpt/sparsedrive_stage1.pth
+  wget https://github.com/swc-17/SparseDrive/releases/download/v1.0/sparsedrive_stage1.pth -O ckpt/sparsedrive_stage1.pth
 ```
 
 ## 训练结果
diff --git a/model_examples/StreamPETR/README.md b/model_examples/StreamPETR/README.md
index 27cba8b87d2377fba93cfe04be32929f6bfa6ab4..4b3b88672ffba7ff74d93ccfdc8a2b37409aa72e 100644
--- a/model_examples/StreamPETR/README.md
+++ b/model_examples/StreamPETR/README.md
@@ -229,10 +229,9 @@ cp -rf test StreamPETR
 
 10. 编译优化
 (1)python编译优化
-源码下载毕昇编译器：
+参考官网下载并安装毕昇编译器4.1.0版本：https://www.hikunpeng.com/zh/developer/devkit/download/bishengcompiler：
 ```
 ulimit -n 4096
-wget --no-check-certificate  https://kunpeng-repo.obs.cn-north-4.myhuaweicloud.com/BiSheng%20Enterprise/BiSheng%20Enterprise%20203.0.0/BiShengCompiler-4.1.0-aarch64-linux.tar.gz
 tar -xvf BiShengCompiler-4.1.0-aarch64-linux.tar.gz
 export PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/bin:$PATH
 export LD_LIBRARY_PATH=$(pwd)/BiShengCompiler-4.1.0-aarch64-linux/lib:$LD_LIBRARY_PATH
@@ -241,7 +240,7 @@ source {cann_root_dir}/set_env.sh
 
 下载所需依赖：
 ```
-wget --no-check-certificate https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
+wget https://www.bytereef.org/software/mpdecimal/releases/mpdecimal-2.5.1.tar.gz
 tar -xvf mpdecimal-2.5.1.tar.gz
 cd mpdecimal-2.5.1
 bash ./configure --prefix=/path/to/install/mpdecimal