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README.md
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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
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More details about the model can be found at: LINK to Tech Report
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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
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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
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that uses AI to
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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
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