初始化项目，由ModelHub XC社区提供模型

Model: Aikyam-Lab/CURE-MED-1.5B
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- reasoning
+- text-generation
+- medical-ai
+- multilingual-ai
+- healthcare
+- LLMs
+license: apache-2.0
+datasets:
+- Aikyam-Lab/CUREMED-BENCH
+language:
+- am
+- bn
+- fr
+- ha
+- hi
+- ja
+- ko
+- es
+- sw
+- th
+- tr
+- vi
+- yo
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: 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.
+
+
+
+![cure_med](https://cdn-uploads.huggingface.co/production/uploads/679a568f79c779b5238c3d71/b0nWUV0Mevcia37j9daK4.png)
+
+## 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
+
+<!-- Provide a longer summary of what this model is. -->
+
+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:**
+```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}
+}
+
+