Each split was fine-tuned from Qwen/Qwen3-4B-Base and placed in its own split_N/ subfolder; per-split checkpoints were selected by Validation AUPRC over a 3-epoch SFT run.

Code, data, & training pipeline: https://github.com/HyeongWon-Jang/TRIAGE

Quick start

python

from transformers import AutoModelForCausalLM, AutoTokenizer

split = "split_1"  # one of split_1 ... split_5
repo  = "Hyeongwon/TRIAGE-4B-P19-SFT"

tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=split)
model     = AutoModelForCausalLM.from_pretrained(repo, subfolder=split, device_map="auto")

The model expects a task-specific input/output template; for the full inference pipeline, see the linked GitHub repository.

Data

Raw: PhysioNet Sepsis Early Prediction Challenge 2019
Processed (Raindrop, CC BY 4.0): figshare DOI

Further preprocessing and split-construction details are in the linked GitHub repository.

Framework versions

Transformers: 4.57.3
PyTorch: 2.6.0
Datasets: 3.6.0
Tokenizers: 0.22.2

License

Model checkpoints are released under CC BY-NC 4.0 (non-commercial). Datasets remain under their respective licenses.