Biomedical LLM with visual capabilities, built on LLaMa-7B
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Visual Med-Alpaca is a parameter-efficient, multimodal foundation model tailored for the biomedical domain, built upon LLaMA-7B. It addresses the high computational costs of training large language models for specialized fields by integrating plug-and-play visual modules, enabling tasks like radiological image interpretation and clinical question answering. The target audience includes researchers and developers working on biomedical AI applications.
How It Works
Visual Med-Alpaca bridges text and vision through a prompt augmentation method. Medical images are processed by specialized "visual experts" (e.g., Med-GIT for radiology, DePlot for charts) which convert visual information into intermediate text formats. This text is then merged with textual queries by a prompt manager, which feeds into the Med-Alpaca LLM for generating domain-specific responses. This modular approach allows for cost-effective integration of diverse visual capabilities.
Quick Start & Requirements
Highlighted Details
Maintenance & Community
The project originates from the Language Technology Lab at the University of Cambridge. Further details on community engagement or ongoing development are not explicitly detailed in the README.
Licensing & Compatibility
The model inherits restrictions from LLaMA. Commercial or clinical use is strictly prohibited. It is intended for academic research purposes only.
Limitations & Caveats
Visual Med-Alpaca is strictly for academic research and not approved for any clinical use. Users are cautioned about potential inaccuracies or misleading medical advice, and reliance for medical decision-making is at the user's own risk.
1 year ago
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