[Doc]: update deployment guide (#591)

README.md

@@ -130,11 +130,13 @@ The effect is similar to below:
 
 We use [LMDeploy](https://github.com/InternLM/LMDeploy) for fast deployment of InternLM.
 
-```shell
-# install LMDeploy
-python3 -m pip install lmdeploy
-# chat with internlm2
-lmdeploy chat turbomind InternLM/internlm2-chat-7b --model-name internlm2-chat-7b
+With only 4 lines of codes, you can perform `internlm2-chat-7b` inference after `pip install lmdeploy`.
+
+```python
+from lmdeploy import pipeline
+pipe = pipeline("internlm/internlm2-chat-7b")
+response = pipe(["Hi, pls intro yourself", "Shanghai is"])
+print(response)
 ```
 
 Please refer to the [guidance](./chat/lmdeploy.md) for more usages about model deployment. For additional deployment tutorials, feel free to explore [here](https://github.com/InternLM/LMDeploy).

README_zh-CN.md

@@ -121,9 +121,13 @@ streamlit run ./chat/web_demo.py
 
 我们使用 [LMDeploy](https://github.com/InternLM/LMDeploy) 完成 InternLM 的一键部署。
 
-```shell
-python3 -m pip install lmdeploy
-lmdeploy chat turbomind InternLM/internlm-chat-7b --model-name internlm-chat-7b
+通过 `pip install lmdeploy` 安装 LMDeploy 之后，只需 4 行代码，就可以实现离线批处理：
+
+```python
+from lmdeploy import pipeline
+pipe = pipeline("internlm/internlm2-chat-7b")
+response = pipe(["Hi, pls intro yourself", "Shanghai is"])
+print(response)
 ```
 
 请参考[部署指南](./chat/lmdeploy.md)了解更多使用案例，更多部署教程则可在[这里](https://github.com/InternLM/LMDeploy)找到。

chat/lmdeploy.md

@@ -0,0 +1,60 @@
+# Inference by LMDeploy
+
+English | [简体中文](lmdeploy_zh_cn.md)
+
+[LMDeploy](https://github.com/InternLM/lmdeploy) is an efficient, user-friendly toolkit designed for compressing, deploying, and serving LLM models.
+
+This article primarily highlights the basic usage of LMDeploy. For a comprehensive understanding of the toolkit, we invite you to refer to [the tutorials](https://lmdeploy.readthedocs.io/en/latest/).
+
+
+## Installation
+
+Install lmdeploy with pip (python 3.8+)
+
+```shell
+pip install lmdeploy
+```
+
+## Offline batch inference
+
+With just 4 lines of codes, you can execute batch inference using a list of prompts:
+
+```python
+from lmdeploy import pipeline
+pipe = pipeline("internlm/internlm2-chat-7b")
+response = pipe(["Hi, pls intro yourself", "Shanghai is"])
+print(response)
+```
+
+With dynamic ntk, LMDeploy can handle a context length of 200K for `InternLM2`:
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig
+engine_config = TurbomindEngineConfig(session_len=200000,
+                                      rope_scaling_factor=2.0)
+pipe = pipeline("internlm/internlm2-chat-7b", backend_engine=engine_config)
+gen_config = GenerationConfig(top_p=0.8,
+                              top_k=40,
+                              temperature=0.8,
+                              max_new_tokens=1024)
+response = pipe(prompt, gen_config=gen_config)
+print(response)
+```
+
+For more information about LMDeploy pipeline usage, please refer to [here](https://lmdeploy.readthedocs.io/en/latest/inference/pipeline.html).
+
+## Serving
+
+LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
+
+```shell
+lmdeploy serve api_server internlm/internlm2-chat-7b
+```
+
+The default port of `api_server` is `23333`. After the server is launched, you can communicate with server on terminal through `api_client`:
+
+```shell
+lmdeploy serve api_client http://0.0.0.0:23333
+```
+
+Alternatively, you can test the server's APIs oneline through the Swagger UI at `http://0.0.0.0:23333`. A detailed overview of the API specification is available [here](https://lmdeploy.readthedocs.io/en/latest/serving/restful_api.html).

chat/lmdeploy_zh_cn.md

@@ -0,0 +1,59 @@
+# LMDeploy 推理
+
+[English](lmdeploy.md) | 简体中文
+
+[LMDeploy](https://github.com/InternLM/lmdeploy) 是一个高效且友好的 LLM 模型部署工具箱，功能涵盖了量化、推理和服务。
+
+本文主要介绍 LMDeploy 的基本用法，包括[安装](#安装)、[离线批处理](#离线批处理)和[推理服务](#推理服务)。更全面的介绍请参考 [LMDeploy 用户指南](https://lmdeploy.readthedocs.io/zh-cn/latest/)。
+
+
+## 安装
+
+使用 pip（python 3.8+）安装 LMDeploy
+
+```shell
+pip install lmdeploy
+```
+
+## 离线批处理
+
+只用以下 4 行代码，就可以完成 prompts 的批处理：
+
+```python
+from lmdeploy import pipeline
+pipe = pipeline("internlm/internlm2-chat-7b")
+response = pipe(["Hi, pls intro yourself", "Shanghai is"])
+print(response)
+```
+
+LMDeploy 实现了 dynamic ntk，支持长文本外推。使用如下代码，可以把 InternLM2 的文本外推到 200K：
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig
+engine_config = TurbomindEngineConfig(session_len=200000,
+                                      rope_scaling_factor=2.0)
+pipe = pipeline("internlm/internlm2-chat-7b", backend_engine=engine_config)
+gen_config = GenerationConfig(top_p=0.8,
+                              top_k=40,
+                              temperature=0.8,
+                              max_new_tokens=1024)
+response = pipe(prompt, gen_config=gen_config)
+print(response)
+```
+
+更多关于 pipeline 的使用方式，请参考[这里](https://lmdeploy.readthedocs.io/zh-cn/latest/inference/pipeline.html)
+
+## 推理服务
+
+LMDeploy `api_server` 支持把模型一键封装为服务，对外提供的 RESTful API 兼容 openai 的接口。以下为服务启动的示例：
+
+```shell
+lmdeploy serve api_server internlm/internlm2-chat-7b
+```
+
+服务默认端口是23333。在 server 启动后，你可以在终端通过`api_client`与server进行对话，体验对话效果：
+
+```shell
+lmdeploy serve api_client http://0.0.0.0:23333
+```
+
+此外，你还可以通过 Swagger UI `http://0.0.0.0:23333` 在线阅读和试用 `api_server` 的各接口，也可直接查阅[文档](https://lmdeploy.readthedocs.io/zh-cn/latest/serving/restful_api.html)，了解各接口的定义和使用方法。