Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- openbmb/UltraInteract
+- openbmb/UltraFeedback
+tags:
+- reasoning
+- preference_learning
+- kto
+pipeline_tag: text-generation
 ---
+
+
+# Links
+
+- 📜 [Paper]()
+- 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5)
+- 🤗 [UltraInteract](https://huggingface.co/datasets/openbmb/UltraInteract)
+
+# Introduction
+
+Eurus-7B-KTO is KTO fine-tuned from Eurus-7B-SFT on all multi-turn trajectory pairs in UltraInteract and all pairs in UltraFeedback.
+
+It achieves the best overall performance among open-source models of similar sizes and even outperforms specialized models in corresponding domains in many cases. Notably, EURUS-7B-KTO outperforms baselines that are 5× larger.
+
+## Usage
+
+We apply tailored prompts for coding and math, consistent with UltraInteract data formats:
+
+**Coding**
+
+```
+[INST] Write Python code to solve the task:
+{Instruction} [/INST]
+```
+**Math-CoT**
+
+```
+[INST] Solve the following math problem step-by-step.
+Simplify your answer as much as possible. Present your final answer as \\boxed{Your Answer}.
+{Instruction} [/INST]
+```
+
+**Math-PoT**
+
+```
+[INST] Tool available:
+[1] Python interpreter
+When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.
+Solve the following math problem step-by-step.
+Simplify your answer as much as possible.
+{Instruction} [/INST]
+```
+
+## Citation
+```
+@misc{yuan2024advancing,
+      title={Advancing LLM Reasoning Generalists with Preference Trees}, 
+      author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun},
+      year={2024},
+      primaryClass={cs.CL}
+}
+```