[Doc] Update README and FAQ (#535)

* update readme

* update readme and faq

README.md

@@ -27,13 +27,16 @@ to **OpenCompass**!
 
 Just like a compass guides us on our journey, OpenCompass will guide you through the complex landscape of evaluating large language models. With its powerful algorithms and intuitive interface, OpenCompass makes it easy to assess the quality and effectiveness of your NLP models.
 
-> **🔥 Attention**<br />
+🔥🔥🔥 We are delighted to announce that **the OpenCompass has been recommended by the Meta AI**, click [Get Started](https://ai.meta.com/llama/get-started/#validation) of Llama for more information.
+
+> **Attention**<br />
 > We launch the OpenCompass Collaboration project, welcome to support diverse evaluation benchmarks into OpenCompass!
 > Clike [Issue](https://github.com/open-compass/opencompass/issues/248) for more information.
 > Let's work together to build a more powerful OpenCompass toolkit!
 
 ## 🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
+- **\[2023.10.24\]** We release a new benchmark for evaluating LLMs’ capabilities of having multi-turn dialogues. Welcome to [BotChat](https://github.com/open-compass/BotChat) for more details. 🔥🔥🔥.
 - **\[2023.09.26\]** We update the leaderboard with [Qwen](https://github.com/QwenLM/Qwen), one of the best-performing open-source models currently available, welcome to our [homepage](https://opencompass.org.cn) for more details. 🔥🔥🔥.
 - **\[2023.09.20\]** We update the leaderboard with [InternLM-20B](https://github.com/InternLM/InternLM), welcome to our [homepage](https://opencompass.org.cn) for more details. 🔥🔥🔥.
 - **\[2023.09.19\]** We update the leaderboard with WeMix-LLaMA2-70B/Phi-1.5-1.3B, welcome to our [homepage](https://opencompass.org.cn) for more details.
@@ -42,7 +45,6 @@ Just like a compass guides us on our journey, OpenCompass will guide you through
 - **\[2023.09.06\]**  [**Baichuan2**](https://github.com/baichuan-inc/Baichuan2) team adpots OpenCompass to evaluate their models systematically. We deeply appreciate the community's dedication to transparency and reproducibility in LLM evaluation.
 - **\[2023.09.02\]** We have supported the evaluation of [Qwen-VL](https://github.com/QwenLM/Qwen-VL) in OpenCompass.
 - **\[2023.08.25\]**  [**TigerBot**](https://github.com/TigerResearch/TigerBot) team adpots OpenCompass to evaluate their models systematically. We deeply appreciate the community's dedication to transparency and reproducibility in LLM evaluation.
-- **\[2023.08.21\]** [**Lagent**](https://github.com/InternLM/lagent) has been released, which is a lightweight framework for building LLM-based agents. We are working with the Lagent team to support the evaluation of general tool-use capability, stay tuned!
 
 > [More](docs/en/notes/news.md)
 
@@ -385,6 +387,8 @@ Through the command line or configuration files, OpenCompass also supports evalu
   </tbody>
 </table>
 
+## OpenCompass Ecosystem
+
 <p align="right"><a href="#top">🔝Back to top</a></p>
 
 ## 📖 Model Support

README_zh-CN.md

@@ -27,13 +27,16 @@
 
 就像指南针在我们的旅程中为我们导航一样，我们希望OpenCompass能够帮助你穿越评估大型语言模型的重重迷雾。OpenCompass提供丰富的算法和功能支持，期待OpenCompass能够帮助社区更便捷地对NLP模型的性能进行公平全面的评估。
 
-> **🔥 注意**<br />
+🔥🔥🔥 祝贺 **OpenCompass 作为大模型标准测试工具被Meta AI官方推荐**, 点击 Llama 的 [入门文档](https://ai.meta.com/llama/get-started/#validation) 获取更多信息.
+
+> **注意**<br />
 > 我们正式启动 OpenCompass 共建计划，诚邀社区用户为 OpenCompass 提供更具代表性和可信度的客观评测数据集!
 > 点击 [Issue](https://github.com/open-compass/opencompass/issues/248) 获取更多数据集.
 > 让我们携手共进，打造功能强大易用的大模型评测平台！
 
 ## 🚀 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>
 
+- **\[2023.10.24\]** 我们发布了一个全新的评测集，BotChat，用于评估大语言模型的多轮对话能力，欢迎查看 [BotChat](https://github.com/open-compass/BotChat) 获取更多信息. 🔥🔥🔥.
 - **\[2023.09.26\]** 我们在评测榜单上更新了[Qwen](https://github.com/QwenLM/Qwen), 这是目前表现最好的开源模型之一, 欢迎访问[官方网站](https://opencompass.org.cn)获取详情.🔥🔥🔥.
 - **\[2023.09.20\]** 我们在评测榜单上更新了[InternLM-20B](https://github.com/InternLM/InternLM), 欢迎访问[官方网站](https://opencompass.org.cn)获取详情.🔥🔥🔥.
 - **\[2023.09.19\]** 我们在评测榜单上更新了WeMix-LLaMA2-70B/Phi-1.5-1.3B, 欢迎访问[官方网站](https://opencompass.org.cn)获取详情.
@@ -42,7 +45,6 @@
 - **\[2023.09.06\]** 欢迎 [**Baichuan2**](https://github.com/baichuan-inc/Baichuan2) 团队采用OpenCompass对模型进行系统评估。我们非常感谢社区在提升LLM评估的透明度和可复现性上所做的努力。
 - **\[2023.09.02\]** 我们加入了[Qwen-VL](https://github.com/QwenLM/Qwen-VL)的评测支持。
 - **\[2023.08.25\]** 欢迎 [**TigerBot**](https://github.com/TigerResearch/TigerBot) 团队采用OpenCompass对模型进行系统评估。我们非常感谢社区在提升LLM评估的透明度和可复现性上所做的努力。
-- **\[2023.08.21\]** [**Lagent**](https://github.com/InternLM/lagent) 正式发布，它是一个轻量级、开源的基于大语言模型的智能体（agent）框架。我们正与Lagent团队紧密合作，推进支持基于Lagent的大模型工具能力评测 !
 
 > [更多](docs/zh_cn/notes/news.md)
 

docs/en/get_started/faq.md

@@ -58,3 +58,22 @@ Each task in OpenCompass represents a combination of specific model(s) and porti
 There is a tradeoff between the number of tasks and the time to load the model. For example, if we partition an request that evaluates a model against a dataset into 100 tasks, the model will be loaded 100 times in total. When resources are abundant, these 100 tasks can be executed in parallel, so the additional time spent on model loading can be ignored. However, if resources are limited, these 100 tasks will operate more sequentially, and repeated loadings can become a bottleneck in execution time.
 
 Hence, if users find that the number of tasks greatly exceeds the available GPUs, we advise setting the `--max-partition-size` to a larger value.
+
+## Model
+
+### How to use the downloaded huggingface models?
+
+If you have already download the checkpoints of the model, you can specify the local path of the model and tokenizer, and add `trust_remote_code=True` for `--model-kwargs` and `--tokenizer-kwargs`. For example
+
+```bash
+python run.py --datasets siqa_gen winograd_ppl \
+--hf-path /path/to/model \  # HuggingFace 模型地址
+--tokenizer-path /path/to/model \  # HuggingFace 模型地址
+--model-kwargs device_map='auto' trust_remote_code=True \  # 构造 model 的参数
+--tokenizer-kwargs padding_side='left' truncation='left' use_fast=False trust_remote_code=True \  # 构造 tokenizer 的参数
+--max-out-len 100 \  # 模型能接受的最大序列长度
+--max-seq-len 2048 \  # 最长生成 token 数
+--batch-size 8 \  # 批次大小
+--no-batch-padding \  # 不打开 batch padding，通过 for loop 推理，避免精度损失
+--num-gpus 1  # 所需 gpu 数
+```

docs/en/notes/news.md

 # News
 
+- **\[2023.08.21\]** [**Lagent**](https://github.com/InternLM/lagent) has been released, which is a lightweight framework for building LLM-based agents. We are working with Lagent team to support the evaluation of general tool-use capability, stay tuned!
 - **\[2023.08.18\]** We have supported evaluation for **multi-modality learning**, include **MMBench, SEED-Bench, COCO-Caption, Flickr-30K, OCR-VQA, ScienceQA** and so on. Leaderboard is on the road. Feel free to try multi-modality evaluation with  OpenCompass !
 - **\[2023.08.18\]** [Dataset card](https://opencompass.org.cn/dataset-detail/MMLU) is now online. Welcome new evaluation benchmark  OpenCompass !
 - **\[2023.08.11\]** [Model comparison](https://opencompass.org.cn/model-compare/GPT-4,ChatGPT,LLaMA-2-70B,LLaMA-65B) is now online. We hope this feature offers deeper insights!

docs/zh_cn/get_started/faq.md

@@ -58,3 +58,22 @@ OpenCompass 中的每个任务代表等待评估的特定模型和数据集部
 任务数量与加载模型的时间之间存在权衡。例如，如果我们将评估模型与数据集的请求分成 100 个任务，模型将总共加载 100 次。当资源充足时，这 100 个任务可以并行执行，所以在模型加载上花费的额外时间可以忽略。但是，如果资源有限，这 100 个任务会更加串行地执行，重复的加载可能成为执行时间的瓶颈。
 
 因此，如果用户发现任务数量远远超过可用的 GPU，我们建议将 `--max-partition-size` 设置为一个较大的值。
+
+## 模型
+
+### 如何使用本地已下好的Huggingface模型?
+
+如果您已经提前下载好Huggingface的模型文件，请手动指定模型路径，并在`--model-kwargs` 和 `--tokenizer-kwargs`中添加 `trust_remote_code=True`. 示例如下
+
+```bash
+python run.py --datasets siqa_gen winograd_ppl \
+--hf-path /path/to/model \  # HuggingFace 模型地址
+--tokenizer-path /path/to/model \  # HuggingFace 模型地址
+--model-kwargs device_map='auto' trust_remote_code=True \  # 构造 model 的参数
+--tokenizer-kwargs padding_side='left' truncation='left' use_fast=False trust_remote_code=True \  # 构造 tokenizer 的参数
+--max-out-len 100 \  # 模型能接受的最大序列长度
+--max-seq-len 2048 \  # 最长生成 token 数
+--batch-size 8 \  # 批次大小
+--no-batch-padding \  # 不打开 batch padding，通过 for loop 推理，避免精度损失
+--num-gpus 1  # 所需 gpu 数
+```

docs/zh_cn/notes/news.md

 # 新闻
 
+- **\[2023.08.21\]** [**Lagent**](https://github.com/InternLM/lagent) 正式发布，它是一个轻量级、开源的基于大语言模型的智能体（agent）框架。我们正与Lagent团队紧密合作，推进支持基于Lagent的大模型工具能力评测 !
 - **\[2023.08.18\]** OpenCompass现已支持**多模态评测**，支持10+多模态评测数据集，包括 **MMBench, SEED-Bench, COCO-Caption, Flickr-30K, OCR-VQA, ScienceQA** 等. 多模态评测榜单即将上线，敬请期待!
 - **\[2023.08.18\]** [数据集页面](https://opencompass.org.cn/dataset-detail/MMLU) 现已在OpenCompass官网上线，欢迎更多社区评测数据集加入OpenCompass !
 - **\[2023.08.11\]** 官网榜单上新增了[模型对比](https://opencompass.org.cn/model-compare/GPT-4,ChatGPT,LLaMA-2-70B,LLaMA-65B)功能，希望该功能可以协助提供更多发现！