CURE-MED-1.5B/README.md

library_name, tags, license, datasets, language, base_model, pipeline_tag

library_name	tags	license	datasets	language	base_model	pipeline_tag
transformers	reasoning text-generation medical-ai multilingual-ai healthcare LLMs	apache-2.0	Aikyam-Lab/CUREMED-BENCH	am bn fr ha hi ja ko es sw th tr vi yo	Qwen/Qwen2.5-3B-Instruct	text-generation

Model Card for Model ID

CURE-MED-1.5B is a 1.5 billion parameter large language model specialized for multilingual medical reasoning, fine-tuned from Qwen/Qwen1.5-1.5B-instruct using a curriculum-informed reinforcement learning framework to enhance logical correctness and language stability in healthcare applications.

Model Details

CURE-MED-1.5B 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 Qwen2.5-1.5B-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:

@article{onyame2026cure,
  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},
  journal={arXiv preprint arXiv:2601.13262},
  year={2026}
}