[Feature] update news (#186)

* update news

* update

---------
Co-authored-by: gaotongxiao <gaotongxiao@gmail.com>

README.md

@@ -29,12 +29,14 @@ Just like a compass guides us on our journey, OpenCompass will guide you through
 
 ## 🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
-- **\[2023.08.07\]** We have added a [script](tools/eval_mmbench.py) for users to evaluate the inference results of [MMBench](https://opencompass.org.cn/MMBench)-dev. 🔥🔥🔥.
-- **\[2023.08.05\]** We have supported [GPT-4](https://openai.com/gpt-4) and [Qwen-7B](https://github.com/QwenLM/Qwen-7B)! Go to our [leaderboard](https://opencompass.org.cn/leaderboard-llm) for more results! More models are welcome to join OpenCompass. 🔥🔥🔥.
-- **\[2023.07.27\]** We have supported [CMMLU](https://github.com/haonan-li/CMMLU)! More datasets are welcome to join OpenCompass. 🔥🔥🔥.
-- **\[2023.07.21\]** Performances of Llama-2 are available in [OpenCompass leaderboard](https://opencompass.org.cn/leaderboard-llm)!  🔥🔥🔥.
-- **\[2023.07.19\]** We have supported [Llama-2](https://ai.meta.com/llama/)! Its performance report will be available soon. \[[Doc](./docs/en/get_started.md#Installation)\] 🔥🔥🔥.
-- **\[2023.07.13\]** We release [MMBench](https://opencompass.org.cn/MMBench), a meticulously curated dataset to comprehensively evaluate different abilities of multimodality models 🔥🔥🔥.
+- **\[2023.08.10\]** OpenCompass is compatible with [LMDeploy](https://github.com/InternLM/lmdeploy). Now you can follow this [instruction](https://opencompass.readthedocs.io/en/latest/advanced_guides/evaluation_turbomind.html#) to evaluate the accelerated models provide by the **Turbomind**. 🔥🔥🔥.
+- **\[2023.08.10\]** We have supported [Qwen-7B](https://github.com/QwenLM/Qwen-7B) and [XVERSE-13B](https://github.com/xverse-ai/XVERSE-13B) ! Go to our [leaderboard](https://opencompass.org.cn/leaderboard-llm) for more results! More models are welcome to join OpenCompass. 🔥🔥🔥.
+- **\[2023.08.09\]** Several new datasets(**CMMLU, TydiQA, SQuAD2.0, DROP**) are updated on our [leaderboard](https://opencompass.org.cn/leaderboard-llm)! More datasets are welcomed to join OpenCompass.
+- **\[2023.08.07\]** We have added a [script](tools/eval_mmbench.py) for users to evaluate the inference results of [MMBench](https://opencompass.org.cn/MMBench)-dev.
+- **\[2023.08.05\]** We have supported [GPT-4](https://openai.com/gpt-4)! Go to our [leaderboard](https://opencompass.org.cn/leaderboard-llm) for more results! More models are welcome to join OpenCompass.
+- **\[2023.07.27\]** We have supported [CMMLU](https://github.com/haonan-li/CMMLU)! More datasets are welcome to join OpenCompass.
+- **\[2023.07.21\]** Performances of Llama-2 are available in [OpenCompass leaderboard](https://opencompass.org.cn/leaderboard-llm)!
+- **\[2023.07.13\]** We release [MMBench](https://opencompass.org.cn/MMBench), a meticulously curated dataset to comprehensively evaluate different abilities of multimodality models.
 
 ## ✨ Introduction
 

README_zh-CN.md

@@ -29,12 +29,15 @@
 
 ## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
-- **\[2023.08.07\]** 新增了 [MMBench 评测脚本](tools/eval_mmbench.py) 以支持用户自行获取 [MMBench](https://opencompass.org.cn/MMBench)-dev 的测试结果. 🔥🔥🔥.
-- **\[2023.08.05\]** [GPT-4](https://openai.com/gpt-4) 与 [Qwen-7B](https://github.com/QwenLM/Qwen-7B) 的评测结果已更新在 OpenCompass [大语言模型评测榜单](https://opencompass.org.cn/leaderboard-llm)! 🔥🔥🔥.
-- **\[2023.07.27\]** 新增了 [CMMLU](https://github.com/haonan-li/CMMLU)! 欢迎更多的数据集加入 OpenCompass. 🔥🔥🔥.
-- **\[2023.07.21\]** Llama-2 的评测结果已更新在 OpenCompass [大语言模型评测榜单](https://opencompass.org.cn/leaderboard-llm)!  🔥🔥🔥.
-- **\[2023.07.19\]** 新增了 [Llama-2](https://ai.meta.com/llama/)！我们近期将会公布其评测结果。\[[文档](./docs/zh_cn/get_started.md#安装)\] 🔥🔥🔥。
-- **\[2023.07.13\]** 发布了 [MMBench](https://opencompass.org.cn/MMBench)，该数据集经过细致整理，用于评测多模态模型全方位能力 🔥🔥🔥。
+- **\[2023.08.10\]** OpenCompass 现已适配 [LMDeploy](https://github.com/InternLM/lmdeploy). 请参考 [评测指南](https://opencompass.readthedocs.io/zh_CN/latest/advanced_guides/evaluation_turbomind.html) 对 **Turbomind** 加速后的模型进行评估. 🔥🔥🔥.
+- **\[2023.08.10\]**  [Qwen-7B](https://github.com/QwenLM/Qwen-7B) 和 [XVERSE-13B](https://github.com/xverse-ai/XVERSE-13B)的评测结果已更新在 OpenCompass [大语言模型评测榜单](https://opencompass.org.cn/leaderboard-llm)! 🔥🔥🔥.
+- **\[2023.08.09\]** 更新更多评测数据集(**CMMLU, TydiQA, SQuAD2.0, DROP**) ，请登录 [大语言模型评测榜单](https://opencompass.org.cn/leaderboard-llm) 查看更多结果! 欢迎添加你的评测数据集到OpenCompass.
+- **\[2023.08.07\]** 新增了 [MMBench 评测脚本](tools/eval_mmbench.py) 以支持用户自行获取 [MMBench](https://opencompass.org.cn/MMBench)-dev 的测试结果.
+- **\[2023.08.05\]** [GPT-4](https://openai.com/gpt-4) 的评测结果已更新在 OpenCompass [大语言模型评测榜单](https://opencompass.org.cn/leaderboard-llm)!
+- **\[2023.07.27\]** 新增了 [CMMLU](https://github.com/haonan-li/CMMLU)! 欢迎更多的数据集加入 OpenCompass.
+- **\[2023.07.21\]** Llama-2 的评测结果已更新在 OpenCompass [大语言模型评测榜单](https://opencompass.org.cn/leaderboard-llm)!
+- **\[2023.07.19\]** 新增了 [Llama-2](https://ai.meta.com/llama/)！我们近期将会公布其评测结果。\[[文档](./docs/zh_cn/get_started.md#安装)\]。
+- **\[2023.07.13\]** 发布了 [MMBench](https://opencompass.org.cn/MMBench)，该数据集经过细致整理，用于评测多模态模型全方位能力。
 
 ## ✨ 介绍
 

docs/en/advanced_guides/evaluation_turbomind.md

@@ -2,13 +2,13 @@
 
 We now support evaluation of models accelerated by the [LMDeploy](https://github.com/InternLM/lmdeploy). LMDeploy is a toolkit designed for compressing, deploying, and serving LLM. **TurboMind** is an efficient inference engine proposed by LMDeploy. OpenCompass is compatible with TurboMind. We now illustrate how to evaluate a model with the support of TurboMind in OpenCompass.
 
-# Setup
+## Setup
 
-## Install OpenCompass
+### Install OpenCompass
 
 Please follow the [instructions](https://opencompass.readthedocs.io/en/latest/get_started.html) to install the OpenCompass and prepare the evaluation datasets.
 
-## Install LMDeploy
+### Install LMDeploy
 
 Install lmdeploy via pip (python 3.8+)
 
@@ -16,11 +16,11 @@ Install lmdeploy via pip (python 3.8+)
 pip install lmdeploy
 ```
 
-# Evaluation
+## Evaluation
 
 We take the InternLM as example.
 
-## Step-1: Get InternLM model
+### Step-1: Get InternLM model
 
 ```shell
 # 1. Download InternLM model(or use the cached model's checkpoint)
@@ -38,13 +38,13 @@ python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-ch
 
 ```
 
-## Step-2: Verify the Converted Model
+### Step-2: Verify the Converted Model
 
 ```shell
 python -m lmdeploy.turbomind.chat ./workspace
 ```
 
-## Step-3: Evaluate the Converted Model
+### Step-3: Evaluate the Converted Model
 
 In the home folder of OpenCompass
 

docs/zh_cn/advanced_guides/evaluation_turbomind.md

@@ -2,13 +2,13 @@
 
 我们支持评测使用[LMDeploy](https://github.com/InternLM/lmdeploy)加速过的大语言模型。LMDeploy 由 MMDeploy 和 MMRazor 团队联合开发，是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。 **TurboMind** 是 LMDeploy 推出的高效推理引擎。OpenCompass 对 TurboMind 进行了适配，本教程将介绍如何使用 OpenCompass 来对 TurboMind 加速后的模型进行评测。
 
-# 环境配置
+## 环境配置
 
-## 安装OpenCompass
+### 安装OpenCompass
 
 请根据OpenCompass[安装指南](https://opencompass.readthedocs.io/en/latest/get_started.html) 来安装算法库和准备数据集。
 
-## 安装LMDeploy
+### 安装LMDeploy
 
 使用pip安装LMDeploy( python 3.8+)
 
@@ -16,11 +16,11 @@
 pip install lmdeploy
 ```
 
-# 评测
+## 评测
 
 我们使用InternLM作为例子来介绍如何评测
 
-## 第一步: 获取InternLM模型
+### 第一步: 获取InternLM模型
 
 ```shell
 # 1. Download InternLM model(or use the cached model's checkpoint)
@@ -38,13 +38,13 @@ python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-ch
 
 ```
 
-## 第二步: 验证转换后的模型
+### 第二步: 验证转换后的模型
 
 ```shell
 python -m lmdeploy.turbomind.chat ./workspace
 ```
 
-## 第三步: 评测转换后的模型
+### 第三步: 评测转换后的模型
 
 在OpenCompass项目文件执行：