diff --git a/README-zh-Hans.md b/README-zh-Hans.md
index 2c1b951..ad19550 100644
--- a/README-zh-Hans.md
+++ b/README-zh-Hans.md
@@ -119,21 +119,22 @@ streamlit run web_demo.py
 
 1. 首先安装 LMDeploy:
 
-  ```
-  python3 -m pip install lmdeploy
-  ```
+   ```bash
+   python3 -m pip install lmdeploy
+   ```
 
 2. 快速的部署命令如下：
 
-  ```
-  python3 -m lmdeploy.serve.turbomind.deploy InternLM-7B /path/to/internlm-7b/model hf
-  ```
+   ```bash
+   python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-7b/model
+   ```
 
-3. 在导出模型后，你可以直接通过如下命令启动服务一个服务并和部署后的模型对话
+3. 在导出模型后，你可以直接通过如下命令启动服务，并在客户端与AI对话
 
-  ```
-  python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
-  ```
+   ```bash
+   bash workspace/service_docker_up.sh
+   python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
+   ```
 
 [LMDeploy](https://github.com/InternLM/LMDeploy) 支持了 InternLM 部署的完整流程，请参考 [部署教程](https://github.com/InternLM/LMDeploy) 了解 InternLM 的更多部署细节。
 
diff --git a/README.md b/README.md
index b7e37c1..090e762 100644
--- a/README.md
+++ b/README.md
@@ -125,21 +125,22 @@ We use [LMDeploy](https://github.com/InternLM/LMDeploy) to complete the one-clic
 
 1. First, install LMDeploy:
 
-```
-  python3 -m pip install lmdeploy
-```
+    ```bash
+    python3 -m pip install lmdeploy
+    ```
 
 2. Use the following command for quick deployment:
 
-```
-  python3 -m lmdeploy.serve.turbomind.deploy InternLM-7B /path/to/internlm-7b/model hf
-```
+    ```bash
+    python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-chat-7b/model
+    ```
 
 3. After exporting the model, you can start a server and have a conversation with the deployed model using the following command:
-
-```
-  python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
-```
+   
+    ```bash
+    bash workspace/service_docker_up.sh
+    python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
+    ```
 
 [LMDeploy](https://github.com/InternLM/LMDeploy) provides a complete workflow for deploying InternLM. Please refer to the [deployment tutorial](https://github.com/InternLM/LMDeploy) for more details on deploying InternLM.