Add pipeline tag, link to paper, and cite the paper

README.md

@@ -1,14 +1,15 @@
 ---
-library_name: transformers
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
 datasets:
 - codeparrot/apps
 - BAAI/TACO
 - AI-MO/NuminaMath-CoT
 language:
 - en
-base_model:
-- Qwen/Qwen2.5-32B-Instruct
+library_name: transformers
 license: apache-2.0
+pipeline_tag: text-generation
 ---
 
 ## Model Details
@@ -50,13 +51,16 @@ We use Llama-Factory for training. On 8 H100, the training takes 19 hours with D
 We would like to thanks the compute resources from [Lambda Lab](https://lambdalabs.com/service/gpu-cloud?srsltid=AfmBOop5FnmEFTkavVtdZDsLWvHWNg6peXtat-OXJ9MW5GMNsk756PE5) and [AnyScale](https://www.anyscale.com/). We would like to thanks the academic feedback and support from the [Still-2 Team](https://arxiv.org/pdf/2412.09413), and [Junyang Lin](https://justinlin610.github.io/) from the [Qwen Team](https://qwenlm.github.io/). 
 
 ## Citation 
-Please considering citing our blog post if you found it useful for your research. Thank you!
+Please considering citing our paper if you found it useful for your research. Thank you!
 
 ```bibtex
-@misc{sky_t1_2025,
-  author       = {NovaSky Team},
-  title        = {Sky-T1: Fully open-source reasoning model with o1-preview performance in $450 budget},
-  howpublished = {https://novasky-ai.github.io/posts/sky-t1},
-  note         = {Accessed: 2025-01-09},
-  year         = {2025}
-}
+@misc{zhu2025llms,
+  author       = {Wenxuan Zhu and Xiangru Tang and Ziyang Ma and Hongbo Zhang and Tianqi Chen},
+  title        = {LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!},
+  year         = {2025},
+  eprint={2502.07374},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url = {https://arxiv.org/abs/2502.07374}
+}
+```