!9968 Fix wrong links in readme.md

From: @lvmingfu
Reviewed-by: @gemini524,@HilbertDavid,@hangangqiang
Signed-off-by: @HilbertDavid

mindspore/lite/README.md

@@ -10,9 +10,9 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
 
 ### MindSpore Lite features
 
-1.  Cooperative work with MindSpore training
-    - Provides training, optimization, and deployment.
-	- The unified IR realizes the device-cloud AI application integration.
+1. Cooperative work with MindSpore training
+   - Provides training, optimization, and deployment.
+   - The unified IR realizes the device-cloud AI application integration.
 
 2. Lightweight
    - Provides model compress, which could help to improve performance as well.
@@ -41,7 +41,7 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
 
 2. Model converter and optimization
 
-   If you use MindSpore or a third-party model, you need to use [MindSpore Lite Model Converter Tool](https://www.mindspore.cn/tutorial/lite/en/master/use/convert_model.html) to convert the model into MindSpore Lite model. The MindSpore Lite model converter tool provides the converter of TensorFlow Lite, Caffe, ONNX to MindSpore Lite model, fusion and quantization could be introduced during convert procedure.
+   If you use MindSpore or a third-party model, you need to use [MindSpore Lite Model Converter Tool](https://www.mindspore.cn/tutorial/lite/en/master/use/converter_tool.html) to convert the model into MindSpore Lite model. The MindSpore Lite model converter tool provides the converter of TensorFlow Lite, Caffe, ONNX to MindSpore Lite model, fusion and quantization could be introduced during convert procedure.
 
    MindSpore also provides a tool to convert models running on IoT devices .
 
@@ -56,6 +56,7 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
    MindSpore provides pre-trained model that can be deployed on mobile device [example](https://www.mindspore.cn/lite/examples/en).
 
 ## MindSpore Lite benchmark test result
+
 Base on MindSpore r0.7, we test a couple of networks on HUAWEI Mate30 (Hisilicon Kirin990) mobile phone, and get the test results below for your reference.
 
 | NetWork             | Thread Number | Average Run Time(ms) |

mindspore/lite/README_CN.md

@@ -5,8 +5,6 @@
 
 MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推理框架，用于满足越来越多的端测AI应用需求。MindSpore Lite聚焦AI技术在端侧设备上的部署和运行，已经在华为HMS和智能终端的图像分类、目标识别、人脸识别、文字识别等应用中广泛使用，未来MindSpore Lite将与MindSpore AI社区一起，致力于丰富AI软硬件应用生态。
 
-
-
 <img src="../../docs/MindSpore-Lite-architecture.png" alt="MindSpore Lite Architecture" width="600"/>
 
 欲了解更多详情，请查看我们的[MindSpore Lite 总体架构](https://www.mindspore.cn/doc/note/zh-CN/master/design/mindspore/architecture_lite.html)。
@@ -49,7 +47,7 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
 
 2. 模型转换/优化
 
-   如果您使用MindSpore或第三方训练的模型，需要使用[MindSpore Lite模型转换工具](https://www.mindspore.cn/tutorial/lite/zh-CN/master/use/convert_model.html)转换成MindSpore Lite模型格式。MindSpore Lite模型转换工具不仅提供了将TensorFlow Lite、Caffe、ONNX等模型格式转换为MindSpore Lite模型格式，还提供了算子融合、量化等功能。
+   如果您使用MindSpore或第三方训练的模型，需要使用[MindSpore Lite模型转换工具](https://www.mindspore.cn/tutorial/lite/zh-CN/master/use/converter_tool.html)转换成MindSpore Lite模型格式。MindSpore Lite模型转换工具不仅提供了将TensorFlow Lite、Caffe、ONNX等模型格式转换为MindSpore Lite模型格式，还提供了算子融合、量化等功能。
 
    MindSpore还提供了将IoT设备上运行的模型转换成.C代码的生成工具。
 
@@ -66,6 +64,7 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
    MindSpore提供了预训练模型部署在智能终端的[样例](https://www.mindspore.cn/lite/examples)。
 
 ## MindSpore Lite性能参考数据
+
 我们在HUAWEI Mate30（Hisilicon Kirin990）手机上，基于MindSpore r0.7，测试了一组端侧常见网络的性能数据，供您参考：
 
 | 网络                | 线程数 | 平均推理时间(毫秒) |

mindspore/lite/examples/train_lenet/README.md

@@ -45,7 +45,9 @@ mnist/
 
 - Server side
     - [MindSpore Framework](https://www.mindspore.cn/install/en): it is recommended to install a docker image
-    - [MindSpore ToD Framework](https://www.mindspore.cn/tutorial/tod/en/use/prparation.html)
+    - MindSpore ToD Framework
+        - [Downloads](https://www.mindspore.cn/tutorial/lite/en/master/use/downloads.html)
+        - [Build](https://www.mindspore.cn/tutorial/lite/en/master/use/build.html)
     - [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
     - [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
 - A connected Android device

mindspore/lite/examples/transfer_learning/README.md

@@ -47,7 +47,9 @@ places
 
 - Server side
     - [MindSpore Framework](https://www.mindspore.cn/install/en) - it is recommended to install a docker image
-    - [MindSpore ToD Framework](https://www.mindspore.cn/tutorial/tod/en/use/prparation.html)
+    - MindSpore ToD Framework
+        - [Downloads](https://www.mindspore.cn/tutorial/lite/en/master/use/downloads.html)
+        - [Build](https://www.mindspore.cn/tutorial/lite/en/master/use/build.html)
     - [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
     - [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
     - [ImageMagick convert tool](https://imagemagick.org/)