Instructions to use aerovane0/GlobalHealthAtlas_Public_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aerovane0/GlobalHealthAtlas_Public_Model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/root/autodl-tmp/LLM/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "aerovane0/GlobalHealthAtlas_Public_Model")

Transformers

How to use aerovane0/GlobalHealthAtlas_Public_Model with Transformers:

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

pipe = pipeline("text-generation", model="aerovane0/GlobalHealthAtlas_Public_Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("aerovane0/GlobalHealthAtlas_Public_Model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aerovane0/GlobalHealthAtlas_Public_Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aerovane0/GlobalHealthAtlas_Public_Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aerovane0/GlobalHealthAtlas_Public_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aerovane0/GlobalHealthAtlas_Public_Model

SGLang

How to use aerovane0/GlobalHealthAtlas_Public_Model 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 "aerovane0/GlobalHealthAtlas_Public_Model" \
    --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": "aerovane0/GlobalHealthAtlas_Public_Model",
		"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 "aerovane0/GlobalHealthAtlas_Public_Model" \
        --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": "aerovane0/GlobalHealthAtlas_Public_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aerovane0/GlobalHealthAtlas_Public_Model with Docker Model Runner:
```
docker model run hf.co/aerovane0/GlobalHealthAtlas_Public_Model
```

GlobalHealthAtlas_Public_Model / README.md

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metadata

base_model: Qwen/Qwen3-8B
library_name: peft
license: mit
pipeline_tag: text-generation
tags:
  - base_model:Qwen/Qwen3-8B
  - llama-factory
  - lora
  - transformers
  - medical
  - public-health
model-index:
  - name: sft100
    results: []

GlobalHealthAtlas Public Model

This repository contains the fine-tuned LoRA adapter for the GlobalHealthAtlas Public Model, introduced in the paper From Knowledge to Inference: Formalizing Specialized Public Health Reasoning on GlobalHealthAtlas.

The model is designed to provide informative, context-aware answers to public health–related queries across 15 domains and 17 languages. It was fine-tuned from Qwen/Qwen3-8B on the GlobalHealthAtlas dataset.

Paper: From Knowledge to Inference: Formalizing Specialized Public Health Reasoning on GlobalHealthAtlas
Repository: Jan8217/GlobalHealthAtlas

Overview

Public health reasoning requires population-level inference grounded in scientific evidence, expert consensus, and safety constraints. This model addresses the scarcity of supervised signals in this domain. It was trained on the GlobalHealthAtlas dataset, a large-scale multilingual corpus of 280,210 instances spanning 15 domains including infectious disease prevention, health policy, and vaccination.

Intended Uses & Limitations

This model is intended to be used as a question-answering and information retrieval component for research and public health queries.

Important Disclaimer: This model is NOT intended for clinical diagnosis, medical advice, or other high‑stakes decision-making without human review by domain experts.

Training Procedure

The model was fine-tuned using LoRA (Low-Rank Adaptation) on the Qwen3-8B base model via the LLaMA Factory framework.

Training Hyperparameters

The following hyperparameters were used during training:

Learning rate: 5e-05
Train batch size: 1
Gradient accumulation steps: 8
Total train batch size: 8
Optimizer: AdamW
LR scheduler type: cosine
LR scheduler warmup ratio: 0.1
Num epochs: 2.0
Seed: 42

Framework Versions

PEFT 0.15.1
Transformers 4.51.3
Pytorch 2.3.0+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Citation

If you use this model or the GlobalHealthAtlas dataset, please cite:

@article{globalhealthatlas2026,
  title={From Knowledge to Inference: Formalizing Specialized Public Health Reasoning on GlobalHealthAtlas},
  author={GlobalHealthAtlas Team},
  year={2026}
}