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README.md
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The model underwent additional training on a proprietary dataset encompassing diverse instruction-following, chat, and function calling data. This post-training process enhances the model's ability to integrate tools and manage complex interaction scenarios effectively.
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## How to Use
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Refer to https://docs.rubra.ai/inference/llamacpp for usage. Feel free to ask/open issues up in our Github repo: https://github.com/rubra-ai/rubra
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The model underwent additional training on a proprietary dataset encompassing diverse instruction-following, chat, and function calling data. This post-training process enhances the model's ability to integrate tools and manage complex interaction scenarios effectively.
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## How to Use
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Refer to https://docs.rubra.ai/inference/llamacpp for usage. Feel free to ask/open issues up in our Github repo: https://github.com/rubra-ai/rubra
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## Limitations and Bias
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While the model performs well on a wide range of tasks, it may still produce biased or incorrect outputs. Users should exercise caution and critical judgment when using the model in sensitive or high-stakes applications. The model's outputs are influenced by the data it was trained on, which may contain inherent biases.
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## Ethical Considerations
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Users should ensure that the deployment of this model adheres to ethical guidelines and consider the potential societal impact of the generated text. Misuse of the model for generating harmful or misleading content is strongly discouraged.
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## Acknowledgements
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We would like to thank Google for the model.
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## Contact Information
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For questions or comments about the model, please reach out to [the rubra team](mailto:rubra@acorn.io).
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## Citation
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If you use this work, please cite it as:
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```
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@misc {rubra_ai_2024,
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author = { Sanjay Nadhavajhala and Yingbei Tong },
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title = { Rubra-Gemma-1.1-2B-IT },
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year = 2024,
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url = { https://huggingface.co/rubra-ai/gemma-1.1-2b-it },
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doi = { 10.57967/hf/2647 },
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publisher = { Hugging Face }
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}
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