Update README.md

README.md

@@ -86,9 +86,9 @@ NVIDIA models are trained on a diverse set of public and proprietary datasets. T
 ## AI Safety Efforts
 
 The Llama-2-13B-DMC-4x model underwent AI safety evaluation including adversarial testing via three distinct methods:
--[Garak](https://github.com/leondz/garak), is an automated LLM vulnerability scanner that probes for common weaknesses, including prompt injection and data leakage.
--[AEGIS](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-1.0), is a content safety evaluation dataset and LLM based content safety classifier model, that adheres to a broad taxonomy of 13 categories of critical risks in human-LLM interactions.
--Human Content Red Teaming leveraging human interaction and evaluation of the models' responses.
+* [Garak](https://github.com/leondz/garak), is an automated LLM vulnerability scanner that probes for common weaknesses, including prompt injection and data leakage.
+* [AEGIS](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-1.0), is a content safety evaluation dataset and LLM based content safety classifier model, that adheres to a broad taxonomy of 13 categories of critical risks in human-LLM interactions.
+* Human Content Red Teaming leveraging human interaction and evaluation of the models' responses.
 
 ## Inference