54 lines
1.7 KiB
Markdown
54 lines
1.7 KiB
Markdown
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---
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language:
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- zh
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- medical
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- health
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- qwen3
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- chat
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- dpo
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- sft
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---
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# G-Health
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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**.
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## Model family
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- **G-Health-14B-Base / G-Health-32B-Base**: medical-domain aligned models on top of Qwen3.
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- **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).
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## Training (brief)
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### Base models (medical-domain alignment)
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Starting from Qwen3, we apply two-stage alignment:
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- **SFT (Supervised Fine-Tuning)**: 2,817,556 dialogue samples
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- **DPO (Direct Preference Optimization)**: 1,643,350 preference samples
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This produces a medical-domain model with improved robustness and communication quality.
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### Instruct models (health checkup specialization)
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On top of the Base models, we perform additional fine-tuning on **health checkup report** data to improve:
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- interpretation of lab values and imaging conclusions
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- cautious risk signaling under uncertainty
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- enhanced personalization awareness for tailoring explanations and recommendations to individual contexts
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## Citation
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```bibtex
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@article{lin2026clinically,
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title={Clinically grounded multi-agent artificial intelligence for preventive health management},
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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},
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year={2026}
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}
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```
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