From 58108413bde753f56363815e2327311ff9db9d3b Mon Sep 17 00:00:00 2001
From: Wenwei Zhang <40779233+ZwwWayne@users.noreply.github.com>
Date: Tue, 22 Aug 2023 07:46:01 +0800
Subject: [PATCH] Update readme for news of InternLM-Chat-7B-v1.1 and Lagent
 (#213)

* update readme

* fix typo
---
 README-zh-Hans.md |  5 +++++
 README.md         | 11 +++++++----
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/README-zh-Hans.md b/README-zh-Hans.md
index ad19550..e7c0a45 100644
--- a/README-zh-Hans.md
+++ b/README-zh-Hans.md
@@ -45,6 +45,10 @@ InternLM ,即书生·浦语大模型,包含面向实用场景的70亿参数
 
 提供了支持模型预训练的轻量级训练框架,无需安装大量依赖包,一套代码支持千卡预训练和单卡人类偏好对齐训练,同时实现了极致的性能优化,实现千卡训练下近90%加速效率。
 
+## 新闻
+
+我们开源了 InternLM-Chat-7B v1.1。该模型能够调用代码解释器和工具插件。你可以在 [Lagent](https://github.com/InternLM/lagent) 中体验这些新功能。
+
 ## InternLM-7B
 
 ### 性能评测
@@ -74,6 +78,7 @@ InternLM ,即书生·浦语大模型,包含面向实用场景的70亿参数
 | 模型                 | InternLM 格式权重下载地址                                                                                                                      | Transformers 格式权重下载地址                    |
 | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------ |
 | **InternLM 7B**      | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b) | [🤗internlm/intern-7b](https://huggingface.co/internlm/internlm-7b) |
+| **InternLM Chat 7B v1.1**    | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-v1.1)    | [🤗internlm/intern-chat-7b-v1.1](https://huggingface.co/internlm/internlm-chat-7b-v1.1)       |
 | **InternLM Chat 7B** | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b) | [🤗internlm/intern-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
 | **InternLM Chat 7B 8k** | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-8k) | [🤗internlm/intern-chat-7b-8k](https://huggingface.co/internlm/internlm-chat-7b-8k)
 
diff --git a/README.md b/README.md
index 090e762..b7cd781 100644
--- a/README.md
+++ b/README.md
@@ -35,9 +35,6 @@
     👋 join us on <a href="https://twitter.com/intern_lm" target="_blank">Twitter</a>, <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=internwx" target="_blank">WeChat</a>
 </p>
 
-
-
-
 ## Introduction
 
 InternLM has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics:
@@ -48,6 +45,11 @@ InternLM has open-sourced a 7 billion parameter base model and a chat model tail
 
 Additionally, a lightweight training framework is offered to support model pre-training without the need for extensive dependencies. With a single codebase, it supports pre-training on large-scale clusters with thousands of GPUs, and fine-tuning on a single GPU while achieving remarkable performance optimizations. InternLM achieves nearly 90% acceleration efficiency during training on 1024 GPUs.
 
+## News
+
+InternLM-7B-Chat v1.1 is released with code interpreter and function calling capability. You can try it with [Lagent](https://github.com/InternLM/lagent)
+-
+
 ## InternLM-7B
 
 ### Performance Evaluation
@@ -77,6 +79,7 @@ InternLM 7B and InternLM 7B Chat, trained using InternLM, have been open-sourced
 | Model                         | InternLM Format Weight Download Link                                                                                                                 | Transformers Format Weight Download Link                                         |
 | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
 | **InternLM 7B**         | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b)         | [🤗internlm/intern-7b](https://huggingface.co/internlm/internlm-7b)                 |
+| **InternLM Chat 7B v1.1**    | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-v1.1)    | [🤗internlm/intern-chat-7b-v1.1](https://huggingface.co/internlm/internlm-chat-7b-v1.1)       |
 | **InternLM Chat 7B**    | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b)    | [🤗internlm/intern-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)       |
 | **InternLM Chat 7B 8k** | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-8k) | [🤗internlm/intern-chat-7b-8k](https://huggingface.co/internlm/internlm-chat-7b-8k) |
 
@@ -136,7 +139,7 @@ We use [LMDeploy](https://github.com/InternLM/LMDeploy) to complete the one-clic
     ```
 
 3. After exporting the model, you can start a server and have a conversation with the deployed model using the following command:
-   
+
     ```bash
     bash workspace/service_docker_up.sh
     python3 -m lmdeploy.serve.client {server_ip_addresss}:33337