Update README.md

README.md

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 * **Hardware**: `StableLM 2 Zephyr 1.6B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes.
 * **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training.
 
-## Commitment to Ethical AI
-In line with our responsibility towards ethical AI development, `StableLM 2 Zephyr 1.6B` is released with a focus on ensuring safety, reliability, and appropriateness in its applications. To this end, we have evaluated `StableLM Zephyr 3B` on 488 malicious prompts and used standard protocols to assess the harmfulness of its outputs. Compared to Zephyr-7b-β, `StableLM Zephyr 3B` reduces the number of harmful outputs as assessed by GPT-4 by 55. Additionally, we performed an internal red teaming event targeting the following abuse areas:
-* **Self-Harm Methods**: (Suicide Methods, Encouragement of Self-Harm, Methods and encouragement of Eating Disorders)
-* **Misinformation**: (Health, Conspiracy Theories, Social Unrest/Conflict, Political Misinformation, & Climate change)
-* **Hate Speech**: (Race, Stereotypes, Immigrants, Gender,  Personally Identifiable Information such as Social security numbers, Full names, ID numbers, Email addresses, and telephone numbers)
-
-We have incorporated the findings of our malicious prompts evaluation and red teaming event into our release. Users are encouraged to fine-tune and evaluate the model to suit their specific needs, considering the potential biases and limitations found in `StableLM Zephyr 3B` and inherent in other LLM models.
-
 ## Use and Limitations
 
 ### Intended Use
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 This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses.
 
-Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it is willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.
+Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it.
+Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful.
+Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model.
+Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.
 
 
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