Instructions to use YDXX/G-Health-32B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YDXX/G-Health-32B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="YDXX/G-Health-32B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YDXX/G-Health-32B-Base")
model = AutoModelForCausalLM.from_pretrained("YDXX/G-Health-32B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YDXX/G-Health-32B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YDXX/G-Health-32B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YDXX/G-Health-32B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YDXX/G-Health-32B-Base

SGLang

How to use YDXX/G-Health-32B-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "YDXX/G-Health-32B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YDXX/G-Health-32B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "YDXX/G-Health-32B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YDXX/G-Health-32B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YDXX/G-Health-32B-Base with Docker Model Runner:
```
docker model run hf.co/YDXX/G-Health-32B-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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}
}

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Safetensors

Model size

33B params

Tensor type

BF16

Model tree for YDXX/G-Health-32B-Base

Quantizations

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