---
license: apache-2.0
base_model: deepseek-ai/deepseek-11m-7b-base
tags:
- phi-deidentification
- healthcare-nlp
- medical-text
- lora
- peft
- privacy
- ner
- safety
---

# DeepSeek PHI De-identification Adapter

This repository hosts a LoRA adapter fine-tuned for safe detection and redaction of
Protected Health Information (PHI) in clinical text.

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"
)