fixed typos

README.md

@@ -17,12 +17,12 @@ base_model:
 - Qwen/Qwen3-4B
 ---
 
-# DynaGuard-8B 🛡️
+# DynaGuard-4B 🛡️
 
 **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-4B)** 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](https://huggingface.co/tomg-group-umd/DynaGuard-8B)** model outperforming other guardian models and even strong generalist models like GPT-4o-mini.
 
 ## Model Details
 
@@ -106,7 +106,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ## Evaluation
 
-DynaGuard-8B achieves state-of-the-art performance, outperforming other dedicated guardian models and strong generalist models like GPT-4o-mini on the DynaBench test set. It also maintains high accuracy on traditional safety benchmarks.
+DynaGuard achieves state-of-the-art performance, outperforming other dedicated guardian models and strong generalist models like GPT-4o-mini on the DynaBench test set. It also maintains high accuracy on traditional safety benchmarks.
 
 | Model | DynaBench (F1) | Safety Tasks Avg (F1) |
 | :--- | :---: | :---: |