Update README.md

README.md

@@ -15,6 +15,9 @@ datasets:
 - tomg-group-umd/DynaBench
 base_model:
 - Qwen/Qwen3-4B
+repo_url: https://github.com/montehoover/DynaGuard
+paper_url: https://arxiv.org/abs/2509.02563
+project_page: https://github.com/taruschirag/DynaGuard
 ---
 
 # DynaGuard-4B 🛡️
@@ -22,9 +25,12 @@ base_model:
 **The DynaGuard model series** is a family of guardian models designed to evaluate text against user-defined, natural language policies. They provide a flexible and powerful solution for moderating chatbot outputs beyond static, predefined harm categories. Developed by researchers at the University of Maryland and Capital One , the series includes three open-weight models of varying sizes:
 1.7B, 4B, and 8B — allowing developers to choose the best balance of performance and efficiency for their needs.
 Unlike traditional guardian models that screen for a fixed set of harms (e.g., violence or self-harm) , DynaGuard can enforce bespoke, application-specific rules. This includes scenarios like preventing a customer service bot from mistakenly issuing refunds or ensuring a medical bot avoids giving unauthorized advice.
-The DynaGuard series achieves state-of-the-art performance across a wide range of safety and compliance benchmarks, with the flagship **[DynaGuard-8B](https://huggingface.co/tomg-group-umd/DynaGuard-8B)** model outperforming other guardian models and even strong generalist models like GPT-4o-mini.
+The DynaGuard series achieves state-of-the-art performance across a wide range of safety and compliance benchmarks, with the flagship **DynaGuard-8B** model outperforming other guardian models and even strong generalist models like GPT-4o-mini.
+
+| 🔖 | 💻 | 🌐 |
+|----|----|---|
+| [Paper (arXiv)](https://arxiv.org/abs/2509.02563) | [Code (GitHub)](https://github.com/montehoover/DynaGuard) | [Project page ](https://github.com/taruschirag/DynaGuard) |
 
-[https://arxiv.org/abs/2509.02563](https://arxiv.org/abs/2509.02563)
 
 
 ## Model Details