Add pipeline tag, library name, and datasets

This PR adds the appropriate `pipeline_tag` (question-answering) and library name (transformers) to the model card. Also adds datasets MATH and GSM8K to the metadata.

README.md

@@ -1,3 +1,18 @@
+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen2.5-Math-7B-Instruct
+library_name: transformers
+pipeline_tag: question-answering
+datasets:
+- MATH
+- GSM8K
+---
+
 Quantization made by Richard Erkhov.
 
 [Github](https://github.com/RichardErkhov)
@@ -47,10 +62,11 @@ metrics:
 base_model:
 - Qwen/Qwen2.5-Math-7B-Instruct
 library_name: transformers
+pipeline_tag: question-answering
 ---
 ## SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights
 
-> [SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights](link)
+> [SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights](https://arxiv.org/abs/2410.09008)
 > [Ling Yang\*](https://yangling0818.github.io/), [Zhaochen Yu*](https://github.com/BitCodingWalkin), [Tianjun Zhang](https://tianjunz.github.io/), [Minkai Xu](https://minkaixu.com/), [Joseph E. Gonzalez](https://people.eecs.berkeley.edu/~jegonzal/),[Bin Cui](https://cuibinpku.github.io/), [Shuicheng Yan](https://yanshuicheng.info/)
 >
 > Peking University, Skywork AI, UC Berkeley, Stanford University 
@@ -152,21 +168,21 @@ We evaluate our SupperCorrect-7B on two widely used English math benchmarks GSM8
 ## Citation
 
 ```bash
-@article{yang2024supercorrect,
-title={SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights}
+@inproceedings{yang2025supercorrect,
+  title={SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights},
   author={Yang, Ling and Yu, Zhaochen and Zhang, Tianjun and Xu, Minkai and Gonzalez, Joseph E and Cui, Bin and Yan, Shuicheng},
-  journal={arXiv preprint arXiv:2410.09008},
-  year={2024}
+  booktitle={International Conference on Learning Representations},
+  year={2025}
 }
+
 @article{yang2024buffer,
   title={Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models},
   author={Yang, Ling and Yu, Zhaochen and Zhang, Tianjun and Cao, Shiyi and Xu, Minkai and Zhang, Wentao and Gonzalez, Joseph E and Cui, Bin},
-  journal={arXiv preprint arXiv:2406.04271},
+  journal={Advances in Neural Information Processing Systems},
   year={2024}
 }
 ```
 
 ## Acknowledgements
 
-Our SuperCorrect is a two-stage fine-tuning model which based on several extraordinary open-source models like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math), [DeepSeek-Math](https://github.com/deepseek-ai/DeepSeek-Math), [Llama3-Series](https://github.com/meta-llama/llama3). Our evaluation method is based on the code base of outstanding works like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math) and  [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). We also want to express our gratitude for amazing works such as [BoT](https://github.com/YangLing0818/buffer-of-thought-llm) which provides the idea of thought template.
-
+Our SuperCorrect is a two-stage fine-tuning model which based on several extraordinary open-source models like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math), [DeepSeek-Math](https://github.com/deepseek-ai/DeepSeek-Math), [Llama3-Series](https://github.com/meta-llama/llama3). Our evaluation method is based on the code base of outstanding works like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math) and  [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). We also want to express our gratitude for amazing works such as [BoT](https://github.com/YangLing0818/buffer-of-thought-llm) which provides the idea of thought template.