Merge remote-tracking branch 'origin/main'

README.md

@@ -1,11 +1,11 @@
 <img src="resources/7D6433A42D189E2E6FBC62BE066BCE91.png">
 
 <p align="center">
-   🌐 <a href="http://keg.cs.tsinghua.edu.cn/glm-130b/posts/glm-130b/" target="_blank">Blog</a> • ⏬ <a href="https://docs.google.com/forms/d/e/1FAIpQLSehr5Dh_i3TwACmFFi8QEgIVNYGmSPwV0GueIcsUev0NEfUug/viewform" target="_blank">Download Model</a> • 🪧 <a href="https://huggingface.co/spaces/THUDM/GLM-130B" target="_blank">Demo</a> • ✉️ <a href="mailto:glm-130b@googlegroups.com">Email</a> • 📃 <a href="http://arxiv.org/abs/2210.02414" target="_blank">Paper</a><br>
+   🌐 <a href="http://keg.cs.tsinghua.edu.cn/glm-130b/posts/glm-130b/" target="_blank">Blog</a> • ⏬ <a href="https://docs.google.com/forms/d/e/1FAIpQLSehr5Dh_i3TwACmFFi8QEgIVNYGmSPwV0GueIcsUev0NEfUug/viewform" target="_blank">Download Model</a> • 🪧 <a href="https://huggingface.co/spaces/THUDM/GLM-130B" target="_blank">Demo</a> • ✉️ <a href="mailto:glm-130b@googlegroups.com">Email</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">Paper</a><br>
 </p>
 
 <p align="center">
-   💬 <a href="https://groups.google.com/g/glm-130b-forum" target="_blank">Google Group</a> (Updates) or <a href="https://join.slack.com/t/glm-130b/shared_invite/zt-1f2ih11xy-EAuDComTAr~XVB3MywE9Cg" target="_blank">Slack channel</a> (Discussions)
+   💬 <a href="https://groups.google.com/g/glm-130b-forum" target="_blank">Google Group</a> (Updates) or <a href="https://github.com/THUDM/GLM-130B/blob/main/resources/WechatGroup.jpeg" target="_blank">Wechat Group</a> or <a href="https://join.slack.com/t/glm-130b/shared_invite/zt-1f2ih11xy-EAuDComTAr~XVB3MywE9Cg" target="_blank">Slack channel</a> (Discussions)
 </p>
 
 # GLM-130B: An Open Bilingual Pre-Trained Model
@@ -21,8 +21,8 @@ GLM-130B is an open bilingual (English & Chinese) bidirectional dense model with
 
 If you find our work and our open-sourced model useful, starring our repo to encourage our following development! :)
 
-## News
-
+## News
+
 - **[2022.10.06]** Our [paper](http://arxiv.org/abs/2210.02414) for GLM-130B is out!
 - **[2022.08.24]** We are proud to publish the quantized version for GLM-130B.  While preserving the activation precision as FP16, the model weights can be quantized to as low as **INT4 with almost no degradation of performance**, further reducing the hardware requirements of the GLM-130B to **a single server with 4 * RTX 3090 (24G)**! See [Quantization of GLM-130B](docs/quantization.md) for details.
 
@@ -50,8 +50,8 @@ It is recommended to use the an A100 (40G * 8) server, as all GLM-130B evaluatio
 The GLM-130B code is built on the top of [SAT](https://github.com/THUDM/SwissArmyTransformer). We recommend using [Miniconda](https://docs.conda.io/en/latest/miniconda.html) to manage your environment and installing additional dependencies via `pip install -r requirements.txt`. Here are the recommended environment configurations:
 
 - Python 3.9+ / CUDA 11+ / PyTorch 1.10+ / DeepSpeed 0.6+ / Apex (**installation with CUDA and C++ extensions is required, see [here](https://github.com/NVIDIA/apex/#linux)**)
-- SwissArmyTransformer>=0.2.11 is required for quantization
-
+- SwissArmyTransformer>=0.2.11 is required for quantization
+
 #### Model weights
 
 Download the GLM-130B’s model checkpoint from [here](https://docs.google.com/forms/d/e/1FAIpQLSehr5Dh_i3TwACmFFi8QEgIVNYGmSPwV0GueIcsUev0NEfUug/viewform?usp=sf_link), make sure all 60 chunks are downloaded completely, then use the following command to merge them into a single archive file and extract it:
@@ -166,9 +166,9 @@ See [Evaluate Your Own Tasks](docs/evaluate-your-own-tasks.md) for details on ho
 ### 2.5X faster Inference using FasterTransformer
 
 By adapting the GLM-130B model to [FasterTransfomer](https://github.com/NVIDIA/FasterTransformer), a highly optimized transformer model library by NVIDIA, we can reach up to 2.5X speedup on generation, see [Inference with FasterTransformer](docs/inference-with-fastertransformer.md) for details.
-
-
-
+
+
+
 
 <details>
 <summary><b>Acknowledgement</b></summary>
@@ -203,3 +203,28 @@ Zhipu.AI
 ## License
 
 This repository is licensed under the [Apache-2.0 license](LICENSE). The use of GLM-130B model weights is subject to the [Model License](MODEL_LICENSE).
+
+## Citation
+
+If you find our work useful, please consider citing GLM-130B:
+
+```
+@article{zeng2022glm130b,
+  title={GLM-130B: An Open Bilingual Pre-trained Model},
+  author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and Tam, Weng Lam and Ma, Zixuan and Xue, Yufei and Zhai, Jidong and Chen, Wenguang and Zhang, Peng and Dong, Yuxiao and Tang, Jie},
+  journal={arXiv preprint arXiv:2210.02414},
+  year={2022}
+}
+```
+
+You may also consider GLM's original work in your reference:
+
+```
+@inproceedings{du2022glm,
+  title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
+  author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
+  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+  pages={320--335},
+  year={2022}
+}
+```