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Model Card: NousResearch/Llama-2-7b-chat-hf Fine-tuned Model

Model Details:

Model Name: NousResearch/Llama-2-7b-chat-hf
Fine-tuned Model Name: llama-2-7b-kiranbeethoju
Dataset Used for Fine-tuning: mlabonne/guanaco-llama2-1k

Model Description:

This model is based on the NousResearch/Llama-2-7b-chat-hf architecture and has been fine-tuned with the dataset mlabonne/guanaco-llama2-1k to enhance its performance for specific tasks.

Intended Use:

This model is intended to be used for natural language processing tasks, particularly in chatbot applications or conversational agents.

Factors to Consider:

Accuracy: The model's accuracy is subject to the quality and representativeness of the fine-tuning dataset.
Bias and Fairness: Care should be taken to assess and mitigate any biases present in both the original model and the fine-tuning dataset.
Safety and Security: As with any AI model, precautions should be taken to ensure that the model is not deployed in contexts where its outputs could cause harm.

Ethical Considerations:

Privacy: It's important to handle user data responsibly and ensure that privacy is maintained when deploying the model in production environments.
Transparency: Users interacting with systems powered by this model should be made aware that they are interacting with an AI system.
Accountability: Clear procedures should be in place to address any issues or errors that arise from the model's use.

Limitations:

The model's performance may vary depending on the similarity of the fine-tuning dataset to the target task or domain.
It may exhibit biases present in the original model or amplified through fine-tuning.

Caveats:

While the model has been fine-tuned for specific tasks, it's essential to conduct thorough testing and validation before deploying it in production environments.

Citation:

If you use this model or the fine-tuned version in your work, please cite the original model as follows:

@article{nousresearch_llama2_2022,
  title={LLAMA: Large Language Model Augmented},
  author={NousResearch},
  journal={GitHub},
  year={2022},
  url={https://github.com/NousResearch/LLAMA}
}

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