初始化项目，由ModelHub XC社区提供模型

Model: YDXX/G-Health-14B-instruct
Source: Original Platform

README.md

@@ -0,0 +1,53 @@
+---
+
+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}
+}
+```