Update README

README.md

@@ -18,6 +18,8 @@ ChatGLM**2**-6B 是开源中英双语对话模型 [ChatGLM-6B](https://github.co
 3. **更高效的推理**：基于 [Multi-Query Attention](http://arxiv.org/abs/1911.02150) 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用：在官方的模型实现下，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K。
 4. **更开放的协议**：ChatGLM2-6B 权重对学术研究**完全开放**，在填写[问卷](https://lslfd0slxc.feishu.cn/share/base/form/shrcnfAj1ZnGOW7kqAfiHFcAFXc)进行登记后**亦允许免费商业使用**。
 
+欢迎通过 [chatglm.cn](https://chatglm.cn) 体验更大规模的 ChatGLM 模型。
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 ChatGLM2-6B 开源模型旨在与开源社区一起推动大模型技术发展，恳请开发者和大家遵守[开源协议](MODEL_LICENSE)，勿将开源模型和代码及基于开源项目产生的衍生物用于任何可能给国家和社会带来危害的用途以及用于任何未经过安全评估和备案的服务。**目前，本项目团队未基于 ChatGLM2-6B 开发任何应用，包括网页端、安卓、苹果 iOS 及 Windows App 等应用。**

README_EN.md

@@ -12,7 +12,9 @@ ChatGLM**2**-6B is the second-generation version of the open-source bilingual (C
 1. **Stronger Performance**: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size.
 2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations.
 3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official  implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K.
-4. **More Open License**: The weights of ChatGLM2-6B are **fully open** to academic research, and with our official written permission, the weights of ChatGLM2-6B are also **permitted for commercial use**. If you find our open-source model useful for your business, we welcome your donation towards the development of the next-generation model ChatGLM3.
+4. **More Open License**: ChatGLM2-6B weights are **completely open** for academic research, and **free commercial use** is also allowed after completing the [questionnaire](https://lslfd0slxc.feishu.cn/share/base/form/shrcnfAj1ZnGOW7kqAfiHFcAFXc).
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+Welcome to use the larger ChatGLM model on [chatglm.cn](https://chatglm.cn) 
 
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@@ -235,7 +237,7 @@ Loading a FP16 ChatGLM-6B model requires about 13GB of memory. Machines with les
 
 ## License
 
-The code of this repository is licensed under [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). The use of the ChatGLM2-6B model weights is subject to the [Model License](MODEL_LICENSE). ChatGLM2-6B weights are **completely open** for academic research, and **commercial use** is also allowed after **obtaining official written permission**. If you find our open source model useful for your business, we welcome your donations towards the development of the next generation model, ChatGLM3. For related matters, please contact [[email protected]](mailto:yiwen.xu@zhipuai.cn).
+The code of this repository is licensed under [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). The use of the ChatGLM2-6B model weights is subject to the [Model License](MODEL_LICENSE). ChatGLM2-6B weights are **completely open** for academic research, and **free commercial use** is also allowed after completing the [questionnaire](https://lslfd0slxc.feishu.cn/share/base/form/shrcnfAj1ZnGOW7kqAfiHFcAFXc).
 
 ## Citation