TRIAGE-4B-P12-SFT

SFT checkpoints released as part of the ${\mathbf{\text{TRIAGE}}}_{\text{SFT}}\backslash textbf\{TRIAGE\}\_\{\backslash text\{SFT\}\}$ work, applied to the P12 dataset.

The model was presented in the paper TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs.

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

from transformers import AutoModelForCausalLM, AutoTokenizer

split = "split_1"  # one of split_1 ... split_5
repo  = "Hyeongwon/TRIAGE-4B-P12-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 Mortality Prediction Challenge 2012
• 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.