Summary of Democratizing Mllms in Healthcare: Tinyllava-med For Efficient Healthcare Diagnostics in Resource-constrained Settings, by Aya El Mir et al.
Democratizing MLLMs in Healthcare: TinyLLaVA-Med for Efficient Healthcare Diagnostics in Resource-Constrained Settings
by Aya El Mir, Lukelo Thadei Luoga, Boyuan Chen, Muhammad Abdullah Hanif, Muhammad Shafique
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an optimization method for large language models (MLLMs) called TinyLLaVA-Med, specifically designed for deployment on resource-constrained devices like the Nvidia Jetson Xavier. The goal is to reduce computational complexity and power consumption while maintaining accurate performance in medical settings. To achieve this, the authors adapt a general-purpose MLLM, TinyLLaVA, through instruction-tuning and fine-tuning on a medical dataset inspired by the LLaVA-Med training pipeline. The result is a model that operates at 18.9W and uses 11.9GB of memory, while achieving state-of-the-art-like accuracies on closed-ended questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TinyLLaVA-Med is an optimized large language model designed for use in medical settings with limited resources. It’s based on the general-purpose MLLM TinyLLaVA, which was adapted and fine-tuned for a specific medical dataset. This new version uses less power (18.9W) and memory (11.9GB) while still performing well on certain tasks. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Large language model » Optimization
