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

  ```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