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- 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
```python
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 |