Update README

README.md

@@ -8,6 +8,15 @@ Both [**YuLan-Mini**](https://huggingface.co/yulan-team/YuLan-Mini) and **YuLan-
 
 This version includes the optimizer, allowing you to resume training using the Hugging Face Trainer and DeepSpeed Universal Checkpoint.
 
+## What you can do with these pre-training resources
+
+1. **Pre-train** your own LLM. You can use [our data](https://huggingface.co/yulan-team/YuLan-Mini-Datasets) and curriculum to train a model that's just as powerful as YuLan-Mini.
+2. Perform your own **learning rate annealing**. During the annealing phase, YuLan-Mini's learning ability is at its peak. You can resume training from [the checkpoint before annealing](https://huggingface.co/yulan-team/YuLan-Mini-Before-Annealing) and use your own dataset for learning rate annealing.
+3. **Fine-tune** the Instruct version of the LLM. You can use the YuLan-Mini base model to train your own Instruct version.
+4. **Training dynamics** research. You can use YuLan-Mini's intermediate checkpoints to explore internal changes during the pre-training process.
+5. **Synthesize** your own data. You can use YuLan-Mini's [data pipeline](https://github.com/RUC-GSAI/YuLan-Mini) to clean and generate your own dataset.
+
+
 ## Continual Training Tutorial
 
 ### Step 1: Modify the `config.json`
@@ -106,3 +115,32 @@ trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
 
 We provide an internal [training framework](https://github.com/RUC-GSAI/YuLan-Mini/tree/main/pretrain) for your reference, but you are free to choose other frameworks.
 
+
+---
+
+## The Team
+
+YuLan-Mini is developed and maintained by [AI Box, Renmin University of China](http://aibox.ruc.edu.cn/).
+
+## License
+
+- The code in this repository is released under the [MIT License](./LICENSE).
+- Policies regarding the use of model weights, intermediate optimizer states, and training data will be announced in future updates.
+- Limitations: Despite our efforts to mitigate safety concerns and encourage the generation of ethical and lawful text, the probabilistic nature of language models may still lead to unexpected outputs. For instance, responses might contain bias, discrimination, or other harmful content. Please refrain from disseminating such content. We are not liable for any consequences arising from the spread of harmful information.
+
+## Citation
+
+If you find YuLan-Mini helpful for your research or development, please cite [our technical report](https://arxiv.org/abs/2412.17743):
+
+```
+@misc{hu2024yulanmini,
+      title={YuLan-Mini: An Open Data-efficient Language Model}, 
+      author={Yiwen Hu and Huatong Song and Jia Deng and Jiapeng Wang and Jie Chen and Kun Zhou and Yutao Zhu and Jinhao Jiang and Zican Dong and Wayne Xin Zhao and Ji-Rong Wen},
+      year={2024},
+      eprint={2412.17743},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.17743}, 
+}
+```
+