Add library_name metadata and link to code

README.md

@@ -1,21 +1,27 @@
 ---
-license: apache-2.0
-language:
-  - en
-tags:
-  - science
-  - hypothesis-generation
-  - biomedical
-  - deepseek
-  - qwen2
 base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+language:
+- en
+license: apache-2.0
 pipeline_tag: text-generation
+library_name: transformers
+tags:
+- science
+- hypothesis-generation
+- biomedical
+- deepseek
+- qwen2
 ---
 
 # MOOSE-Star-HC-R1D-7B
 
 **MOOSE-Star Hypothesis Composition model** — a 7B model fine-tuned for generating scientific hypotheses from research questions, background surveys, and cross-paper inspirations.
 
+This model was introduced in the paper [MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier](https://arxiv.org/abs/2603.03756).
+
+- **Code**: [ZonglinY/MOOSE-Star](https://github.com/ZonglinY/MOOSE-Star)
+- **Paper**: [arXiv:2603.03756](https://arxiv.org/abs/2603.03756)
+
 > **Note**: This model is referred to as **MS-HC-7B (w/ 1x bounded)** in the paper. The full name includes "R1D" to indicate it is fine-tuned from DeepSeek-R1-Distill-Qwen-7B; the SFT data can be used to train other base models as well.
 
 ## Model Description
@@ -24,7 +30,6 @@ pipeline_tag: text-generation
 - **Training Method**: Full-parameter SFT (ZeRO-3)
 - **Training Data**: [TOMATO-Star-SFT-Data-R1D-32B](https://huggingface.co/datasets/ZonglinY/TOMATO-Star-SFT-Data-R1D-32B) HC split (114,548 samples = 96,879 normal + 17,669 bounded, mixed 1x)
 - **Teacher Model**: Training data generated via rejection sampling with [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)
-- **Paper**: [MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier](https://arxiv.org/abs/2603.03756)
 
 ## Training Configuration
 
@@ -251,10 +256,11 @@ Scores on a rubric scale. "Total" aggregates Motivation (Mot), Mechanism (Mec),
 @article{yang2025moosestar,
   title={MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier},
   author={Yang, Zonglin and Bing, Lidong},
+  journal={arXiv preprint arXiv:2603.03756},
   year={2025}
 }
 ```
 
 ## License
 
-This model is released under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.
+This model is released under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.