AdilA1016/esi1trainedmodel

language: - en tags: - peft - lora - medical - triage - emergency - text-classification base_model: google/medgemma-4b-it library_name: peft pipeline_tag: text-classification license: mit

ESI-1 LoRA Adapter (MIETIC) for MedGemma 4B

Model Summary

This repository contains a LoRA adapter (not a full standalone model) for ESI-1 prediction in emergency triage settings. The adapter is trained on MIETIC using few-shot, parameter-efficient fine-tuning (PEFT) on top of MedGemma 4B (google/medgemma-4b-it).

Model Details

• Model type: LoRA adapter
• Base model: google/medgemma-4b-it
• Task: ESI-1 prediction (emergency severity triage)
• Training approach: Specialized few-shot PEFT
• Repository owner: AdilA1016

Files in this Repo

• adapter_config.json
• adapter_model.safetensors
• chat_template.jinja
• processor_config.json
• tokenizer_config.json
• tokenizer.json

Intended Use

This model is intended for research and decision-support prototyping for emergency triage workflows. It is not intended to replace clinician judgment.

Out-of-Scope / Limitations

• Not validated as an autonomous clinical decision maker.
• Performance may vary by site, population, and documentation style.
• Should not be used as the sole basis for real-time medical decisions.

Training Data

• Dataset: MIETIC
• Domain: Emergency/clinical triage text
• Label focus: ESI-1 identification

Add a short description of MIETIC access/curation and any preprocessing steps you applied.

Training Procedure

• Method: LoRA fine-tuning on MedGemma 4B
• Regime: Few-shot specialized adaptation
• Frameworks: PEFT + Transformers
• Hardware: [fill in]
• Epochs / steps: [fill in]
• Learning rate: [fill in]
• Batch size: [fill in]
• LoRA config (r, alpha, target modules): [fill in]

Evaluation

• Validation setup: [fill in]
• Primary metrics: [fill in, e.g., recall/precision/F1 for ESI-1]
• Key results: [fill in]
• Failure modes observed: [fill in]

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "google/medgemma-4b-it"
adapter_id = "AdilA1016/esi1trainedmodel"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(base_id)
model = PeftModel.from_pretrained(base_model, adapter_id)

## Safety and Ethics

This model operates in a high-stakes medical context. Outputs may be incorrect, incomplete, or biased.
Human oversight by qualified clinicians is required for any practical use.

## Citation

If you use this adapter, please cite:

- MIETIC dataset/source: [fill in]
- MedGemma base model: [fill in official citation/link]
- This repository: AdilA1016/esi1trainedmodel