cxllin/Llama2-7b-med-v1

Model Details

Description

The cxllin/Llama2-7b-med-v1 model, derived from the Llama 7b model, is posited to specialize in Natural Language Processing tasks within the medical domain.

Development Details

• Developer: Collin Heenan
• Model Architecture: Transformer
• Base Model: Llama-2-7b
• Primary Language: English
• License: apache 2.0

Model Source Links

• Repository: Not Specified
• Paper: Jin, Di, et al. "What Disease does this Patient Have?..."

Direct Applications

The model is presumed to be applicable for various NLP tasks within the medical domain, such as:

• Medical text generation or summarization.
• Question answering related to medical topics.

Downstream Applications

Potential downstream applications might encompass:

• Healthcare chatbot development.
• Information extraction from medical documentation.

Out-of-Scope Utilizations

• Rendering definitive medical diagnoses or advice.
• Employing in critical healthcare systems without stringent validation.
• Applying in any high-stakes or legal contexts without thorough expert validation.

Bias, Risks, and Limitations

• Biases: The model may perpetuate biases extant in the training data, influencing neutrality.
• Risks: There exists the peril of disseminating inaccurate or misleading medical information.
• Limitations: Expertise in highly specialized or novel medical topics may be deficient.

Recommendations for Use

Utilizers are urged to:

• Confirm outputs via expert medical review, especially in professional contexts.
• Employ the model judiciously, adhering to pertinent legal and ethical guidelines.
• Maintain transparency with end-users regarding the model’s capabilities and limitations.

Getting Started with the Model

Details regarding model deployment and interaction remain to be provided.

Training Dataset

• Dataset Source:cxllin/medinstruct
• Size: 10.2k rows
• Scope: Medical exam-related question-answering data.

Preprocessing Steps

Details regarding data cleaning, tokenization, and special term handling during training are not specified.

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@article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }