The model is trained on a large synthetic and de-identified corpus derived from MIMIC-III-style clinical notes and is designed to operate as part of a configurable, explainable medical text de-identification pipeline.

Model Details

Developed by: Iftakhar Khandokar (Marquette University)
Funded by: Academic research (EECE Department, Marquette University)
Shared by: Iftakhar Khandokar
Model type: LoRA adapter (PEFT)
Base model: deepseek-ai/deepseek-11m-7b-base
Language: English (clinical / biomedical NLP)
License: Apache 2.0

Intended Use

This adapter is intended for:

✅ Research on medical data de-identification
✅ Benchmarking privacy-preserving NLP pipelines
✅ Safety and explainability evaluation for clinical LLM workflows

Not Intended For

❌ Automated medical diagnosis
❌ Direct patient care deployment without regulatory review
❌ Generating synthetic patient records for real-world use

Loading the Model

python

from transformers import AutoModelForCausalLM
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained(
    "deepseek-ai/deepseek-11m-7b-base", trust_remote_code=True
)

model = PeftModel.from_pretrained(
    base,
    "Iftakhar/deepseek-phi-adapter"
)