language, library_name, pipeline_tag, tags

language	library_name	pipeline_tag	tags
zh	transformers	text-generation	medical health qwen3 chat dpo sft

G-Health

G-Health is a family of large language models for medical and preventive health use cases. Built on Qwen3, the models are aligned with large-scale medical dialogues and further adapted for health checkup report interpretation.

Model family

• G-Health-14B-Base / G-Health-32B-Base: medical-domain aligned models on top of Qwen3.
• G-Health-14B-instruct / G-Health-32B-instruct: built on the corresponding Base models, then fine-tuned specifically for health checkup reports (more structured report-to-action outputs).

Training (brief)

Base models (medical-domain alignment)

Starting from Qwen3, we apply two-stage alignment:

• SFT (Supervised Fine-Tuning): 2,817,556 dialogue samples
• DPO (Direct Preference Optimization): 1,643,350 preference samples

This produces a medical-domain model with improved robustness and communication quality.

Instruct models (health checkup specialization)

On top of the Base models, we perform additional fine-tuning on health checkup report data to improve:

• interpretation of lab values and imaging conclusions
• cautious risk signaling under uncertainty
• enhanced personalization awareness for tailoring explanations and recommendations to individual contexts

Citation

@article{lin2026clinically,
  title={Clinically grounded multi-agent artificial intelligence for preventive health management},
  author={Lin, Hao and Zhang, Yang and Ye, Dongxin and He, Sicheng and Du, Zhaowu and Yu, Yang and Yu, Xiao and Ren, Liping and Dong, Nanqing and Hu, Fang and others},
  year={2026}
}