Aikyam-Lab/CURE-MED-14B

Model Card for Model ID

CURE-MED-14B is a 14 billion parameter large language model specialized for multilingual medical reasoning, fine-tuned from Qwen/Qwen2.5-14B using a curriculum-informed reinforcement learning framework to enhance logical correctness and language stability in healthcare applications.

Model Details

CURE-MED-14B 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 Qwen/Qwen2.5-14B-Instruct 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.

Model Description

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
• Model type: Multilingual medical reasoning large language model
• Language(s) (NLP): Amharic, Bengali, French, Hausa, Hindi, Japanese, Korean, Spanish, Swahili, Thai, Turkish, Vietnamese, Yoruba
• License: Apache 2.0
• Finetuned from model: Qwen2.5-Instruct (1.5B, 3B, 7B, 14B, 32B variants)

Model Sources

• Repository: https://github.com/AikyamLab/cure-med
• Paper: https://arxiv.org/abs/2601.13262
• Demo: https://cure-med.github.io/

Citation

BibTeX:

@misc{onyame2026curemedcurriculuminformedreinforcementlearning,
  title        = {CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning},
  author       = {Onyame, Eric and Ghosh, Akash and Baidya, Subhadip and Saha, Sriparna and Chen, Xiuying and Agarwal, Chirag},
  year         = {2026},
  eprint       = {2601.13262},
  archivePrefix= {arXiv},
  primaryClass = {cs.AI},
  url          = {https://arxiv.org/abs/2601.13262}
}