diff --git a/README-ja-JP.md b/README-ja-JP.md
index 40a1167..5e5b7db 100644
--- a/README-ja-JP.md
+++ b/README-ja-JP.md
@@ -123,20 +123,20 @@ streamlit run web_demo.py
 
 1. まず、LMDeploy をインストールする:
 
-```
-  python3 -m pip install lmdeploy
+```shell
+python3 -m pip install lmdeploy
 ```
 
 2. クイックデプロイには以下のコマンドを使用します:
 
-```
-  python3 -m lmdeploy.serve.turbomind.deploy InternLM-7B /path/to/internlm-7b/model hf
+```shell
+lmdeploy chat turbomind InternLM/internlm-chat-7b --model-name internlm-chat-7b
 ```
 
 3. モデルをエクスポートした後、以下のコマンドを使ってサーバーを起動し、デプロイされたモデルと会話することができます:
 
-```
-  python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
+```shell
+lmdeploy serve api_server InternLM/internlm-chat-7b --model-name internlm-chat-7b
 ```
 
 [LMDeploy](https://github.com/InternLM/LMDeploy) は、InternLM をデプロイするための完全なワークフローを提供します。InternLM のデプロイの詳細については、[デプロイチュートリアル](https://github.com/InternLM/LMDeploy)を参照してください。
diff --git a/README-zh-Hans.md b/README-zh-Hans.md
index f78e5f1..62d58d0 100644
--- a/README-zh-Hans.md
+++ b/README-zh-Hans.md
@@ -213,23 +213,22 @@ streamlit run web_demo.py
 
 1. 首先安装 LMDeploy:
 
-  ```
+  ```shell
   python3 -m pip install lmdeploy
   ```
+2. 直接在本地，通过命令行，交互式和 InternLM 对话：
 
-2. 快速的部署命令如下：
-
-  ```
-  python3 -m lmdeploy.serve.turbomind.deploy InternLM-7B /path/to/internlm-7b/model hf
+  ```shell
+  lmdeploy chat turbomind InternLM/internlm-chat-7b --model-name internlm-chat-7b
   ```
 
-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
+1. 也可以使用如下命令启动推理服务：
 
+  ```shell
+  lmdeploy serve api_server InternLM/internlm-chat-7b --model-name internlm-chat-7b
   ```
-  python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
-  ```
+请参考[此指南](https://github.com/InternLM/lmdeploy/blob/main/docs/en/restful_api.md)获取详细的api_server RESTful API信息，更多部署教程则可在[这里](https://github.com/InternLM/LMDeploy)找到。
 
-[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。
 
 ## 微调&训练
 
diff --git a/README.md b/README.md
index 76b5987..e509963 100644
--- a/README.md
+++ b/README.md
@@ -212,23 +212,22 @@ We use [LMDeploy](https://github.com/InternLM/LMDeploy) to complete the one-clic
 
 1. First, install LMDeploy:
 
-```
-  python3 -m pip install lmdeploy
+```shell
+python3 -m pip install lmdeploy
 ```
 
-2. Use the following command for quick deployment:
+2. Use the following command for iteractive communication with `internlm-chat-7b` model on localhost:
 
-```
-  python3 -m lmdeploy.serve.turbomind.deploy InternLM-7B /path/to/internlm-7b/model hf
+```shell
+lmdeploy chat turbomind InternLM/internlm-chat-7b --model-name internlm-chat-7b
 ```
 
-3. After exporting the model, you can start a server and have a conversation with the deployed model using the following command:
+3. Besides chatting via command line, you can start lmdeploy `api_server` as below:
 
+```shell
+lmdeploy serve api_server InternLM/internlm-chat-7b --model-name internlm-chat-7b
 ```
-  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.
+For a comprehensive understanding of the `api_server` RESTful API, kindly consult [this](https://github.com/InternLM/lmdeploy/blob/main/docs/en/restful_api.md) guide. For additional deployment tutorials, feel free to explore [here](https://github.com/InternLM/LMDeploy).
 
 ## Fine-tuning & Training