This is a fully merged, standalone model fine-tuned from unsloth/medgemma-4b-pt for ECG interpretation and clinical report generation. It was trained using the Unsloth library for high-efficiency, memory-optimized fine-tuning. This model is designed to take structured output from a primary ML classifier (which provides findings like «Atrial Fibrillation: 82% confidence, Present») and synthesize it into a coherent, human-readable clinical report, complete with an impression, detailed analysis, and clinical recommendations. — Base Model: unsloth/medgemma-4b-pt — Fine-tuning Method: Unsloth + LoRA (merged into base model) — Training Data: 500 curated ECG interpretation examples. — Evaluation Score: The model achieved an average structural correctness score of 1.000 / 1.0 on a hold-out set. This model follows a standard instruction format. Provide the instruction and the structured input to get a clinical report. This model is intended for research and development purposes and is not a substitute for professional medical advice.
Модальности:
Генерация текста
Области применения:
Медицина
Задача: Генерация текста
Автор: OussamaEL
Теги: tensorboard, gemma3, image-text-to-text, medical, ecg, cardiology, report-generation, unsloth
Лайков: 4 | Загрузок: 27
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
