CURE-MED-7B is a 7 billion parameter large language model specialized for multilingual medical reasoning, fine-tuned from Qwen/Qwen2.5-7B using a curriculum-informed reinforcement learning framework to enhance logical correctness and language stability in healthcare applications. CURE-MED-7B is part of the CURE-MED family of models, designed to address the challenges of multilingual medical reasoning in large language models (LLMs). Built on the Qwen2.5-7B base model, it incorporates a curriculum-informed reinforcement learning approach that integrates code-switching-aware supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) to improve performance on open-ended medical queries across 13 languages, including underrepresented ones such as Amharic, Yoruba, and Swahili. The model is trained and evaluated using CUREMED-BENCH, a high-quality multilingual open-ended medical reasoning benchmark with single verifiable answers. This is the model card of a 🤗 transformers model that has been pushed on the Hub. — Developed by: Eric Onyame, Akash Ghosh, Subhadip Baidya, Sriparna Saha, Xiuying Chen, Chirag Agarwal (Aikyam Lab and collaborators) — Shared by: Aikyam Lab -…
Модальности:
Генерация текста
Области применения:
Логика и рассуждение Диалог / чат
Задача: Генерация текста
Автор: Aikyam-Lab
Теги: qwen2, reasoning, medical-ai, multilingual-ai, healthcare, LLMs, conversational, am
Лайков: 4 | Загрузок: 363
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
