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@@ -23,14 +23,14 @@ We open-source Orca 2 to encourage further research on the development, evaluati
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  ## How was Orca 2 evaluated?
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- + Orca 2 has been evaluated on a large number of tasks ranging from reasoning to safety. Please refer to Section 6 and Appendix in the paper for details on evaluations.
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  ## Model Details
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- Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was filtered using the Azure content filters.
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  More details about the model can be found at: LINK to Tech Report
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- Refer to LLaMA-2 for details on model architectures.
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  ## License
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@@ -41,7 +41,7 @@ Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/ll
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  ## Bias, Risks, and Limitations
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  Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
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- common limitations of other large language models or limitation including by its training
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  process, including:
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  **Data Biases**: Large language models, trained on extensive data, can inadvertently carry
@@ -148,9 +148,9 @@ print(second_turn_answer)
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  **Safe inference with Azure AI Content Safety**
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  The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged
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- and can help prevent content harms. Azure AI Content Safety is a content moderation platform
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- that uses AI to keep your content safe. By integrating Orca 2 with Azure AI Content Safety,
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- we can moderate the model output by scanning it for sexual content, violence, hate, and
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  self-harm with multiple severity levels and multi-lingual detection.
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  ```python
 
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  ## How was Orca 2 evaluated?
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+ + Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer to Section 6 and Appendix in the paper for details on evaluations.
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  ## Model Details
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+ Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters.
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  More details about the model can be found at: LINK to Tech Report
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+ Please refer to LLaMA-2 technical report for details on the model architecture.
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  ## License
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  ## Bias, Risks, and Limitations
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  Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
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+ common limitations of other large language models or limitation caused by its training
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  process, including:
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  **Data Biases**: Large language models, trained on extensive data, can inadvertently carry
 
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  **Safe inference with Azure AI Content Safety**
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  The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged
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+ and can help preventing some of content harms. Azure AI Content Safety is a content moderation platform
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+ that uses AI to moderate content. By having Azure AI Content Safety on the output of Orca 2,
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+ the model output can be moderated by scanning it for different harm categories including sexual content, violence, hate, and
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  self-harm with multiple severity levels and multi-lingual detection.
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  ```python