add link to GLM

README.md

@@ -7,7 +7,7 @@
 
 # GLM-130B: An Open Bilingual Pre-Trained Model
 
-GLM-130B is an open bilingual (English & Chinese) bidirectional dense model with 130 billion parameters, pre-trained using the algorithm of General Language Model (GLM). It is designed to support inference tasks with the 130B parameters on **a single A100 (40G * 8)** or **V100 (32G * 8) server**. As of July 3rd, 2022, GLM-130B has been trained on over 400 billion text tokens (200B each for Chinese and English) and it has the following unique features:
+GLM-130B is an open bilingual (English & Chinese) bidirectional dense model with 130 billion parameters, pre-trained using the algorithm of [General Language Model (GLM)](https://aclanthology.org/2022.acl-long.26). It is designed to support inference tasks with the 130B parameters on **a single A100 (40G * 8)** or **V100 (32G * 8) server**. As of July 3rd, 2022, GLM-130B has been trained on over 400 billion text tokens (200B each for Chinese and English) and it has the following unique features:
  
 - **Bilingual:** supports both English and Chinese. 
 - **Performance (EN):** better than GPT-3 175B (+4.0%), OPT-175B (+5.5%), and BLOOM-176B (+13.0%) on LAMBADA and slightly better than GPT-3 175B (+0.9%) on MMLU.
@@ -21,18 +21,18 @@ GLM-130B is an open bilingual (English & Chinese) bidirectional dense model with
 ### Environment Setup
 
 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)**)
+
+- 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)**)
     
-It is recommended to use the an A100 (40G * 8) server, as all GLM-130B evaluation results (~30 tasks) reported can be easily reproduced with a single A100 server in about half a day. GLM-130B can also be inferenced on servers with smaller GPU memory, such as a V100 (32G * 8) server. See [Low-Resource Inference](docs/low-resource-inference.md) for details.
-
-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:
-
-```bash
-cat glm-130b-sat.tar.part_* > glm-130b-sat.tar
-tar xvf glm-130b-sat.tar
-```
-
+It is recommended to use the an A100 (40G * 8) server, as all GLM-130B evaluation results (~30 tasks) reported can be easily reproduced with a single A100 server in about half a day. GLM-130B can also be inferenced on servers with smaller GPU memory, such as a V100 (32G * 8) server. See [Low-Resource Inference](docs/low-resource-inference.md) for details.
+
+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:
+
+```bash
+cat glm-130b-sat.tar.part_* > glm-130b-sat.tar
+tar xvf glm-130b-sat.tar
+```
+
 Set `CHECKPOINT_PATH` in `configs/model_glm_130b.sh` to the path of the extracted folder. Since the checkpoint file is up to 260G, it is recommended to use the SSD or RAM disk to reduce the checkpoint loading time.
 
 ### Left-To-Right Generation / Blank Filling
@@ -367,7 +367,7 @@ Zhipu.AI
 
 ### Project Leader
 [Jie Tang (Tsinghua KEG & BAAI)](http://keg.cs.tsinghua.edu.cn/jietang/)
-
-## 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).
+
+## 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).