---

language:

- zh
library_name: transformers
pipeline_tag: text-generation
tags:
- 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
```bibtex
@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}
}
```
