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Update README.md

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@@ -9,17 +9,14 @@ datasets:
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  inference: true
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  language:
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  - en
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- license: other
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  model_creator: Stability AI
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  model_name: StableLM Zephyr 3B
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  model_type: stablelm
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- prompt_template: '<|user|>
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-
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  {prompt}<|endoftext|>
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-
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  <|assistant|>
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-
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- '
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  quantized_by: TheBloke
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  tags:
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  - causal-lm
@@ -511,4 +508,4 @@ The model is intended to be used as a foundational base model for application-sp
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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.
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- 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.
 
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  inference: true
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  language:
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  - en
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+ license: apache-2.0
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  model_creator: Stability AI
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  model_name: StableLM Zephyr 3B
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  model_type: stablelm
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+ prompt_template: |
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+ <|user|>
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  {prompt}<|endoftext|>
 
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  <|assistant|>
 
 
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  quantized_by: TheBloke
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  tags:
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  - causal-lm
 
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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.
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+ 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.