Update README.md

README.md

@@ -14,8 +14,6 @@ The model has hybrid architecture with Mamba and Attention heads running in para
 
 This model is ready for commercial use.
 
-**[Model Weights Coming Soon, expected Nov 25th]**
-
 **[Caution] During generation, the batch size needs to be 1. Our current implementation does not fully support padding of Meta tokens + SWA; this is a work in progress. Training and pre-filling support any batch size.**
 
 
@@ -97,8 +95,12 @@ print(f"Model response: {response}")
 
 ```
 
-## Limitations 
-The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. 
+
+## Limitations
+
+The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
+
+Garak/AEGIS testing suggests that this model is susceptible to jailbreak attacks. If using this model in a RAG or agentic setting, we recommend strong output validation controls to ensure security and safety risks from user-controlled model outputs are consistent with the intended use cases.
 
 ## Ethical Considerations 
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.