🏥 gemma4-e4b-icd-coding

A fine-tuned Gemma 4 E4B model for automatic ICD code prediction from clinical notes. Given a free-text clinical note, the model outputs the relevant ICD diagnosis codes — streamlining medical billing, documentation, and clinical analytics workflows.

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📌 Model Overview

Property	Details
Base Model	unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
Fine-tuned by	nikhil061307
Task	Clinical Note → ICD Code Prediction
Language	English
License	Apache 2.0
Training Framework	Unsloth + HuggingFace TRL

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🚀 What It Does

Given a clinical note like:

"Patient presents with persistent cough, fever, and bilateral infiltrates on chest X-ray. Diagnosed with community-acquired pneumonia."

The model outputs the appropriate ICD-10 code(s), e.g.:

J18.9 - Pneumonia, unspecified organism

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💻 Usage

Installation

pip install unsloth transformers torch

Inference

from transformers import AutoTokenizer, AutoModelForImageTextToText
import torch

model_id = "nikhil061307/gemma4-e4b-icd-coding"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

clinical_note = """
Patient is a 65-year-old male with a history of type 2 diabetes presenting 
with polyuria, polydipsia, and HbA1c of 9.2%. Blood glucose fasting at 210 mg/dL.
"""

messages = [
    {
        "role": "user",
        "content": f"Predict the ICD-10 codes for the following clinical note:\n\n{clinical_note}"
    }
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.1,
        do_sample=True,
    )

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)

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🧪 Example Input / Output

Input (Clinical Note):

A 52-year-old woman presents with sharp chest pain radiating to the left arm,
diaphoresis, and shortness of breath. ECG shows ST elevation in leads II, III, aVF.
Troponin elevated. Impression: Acute inferior STEMI.

Output (ICD Codes):

I21.19 - ST elevation (STEMI) myocardial infarction involving other coronary artery

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⚙️ Training Details

• Base model: unsloth/gemma-4-e4b-it-unsloth-bnb-4bit (4-bit quantized)
• Training speedup: 2x faster training with Unsloth
• Library: HuggingFace TRL (SFTTrainer)
• Quantization: BnB 4-bit (inference efficient)

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⚠️ Limitations & Disclaimer

• This model is intended for research and assistive purposes only.
• It is not a substitute for professional medical coding by certified coders (CPC/CCS).
• Always verify predicted ICD codes with qualified clinical staff before use in billing or official documentation.
• Model performance may vary across specialties, note styles, and rare diagnosis categories.

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📄 License

This model is released under the Apache 2.0 license. See LICENSE for details.

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🙏 Acknowledgements

• Unsloth AI — for the blazing fast fine-tuning framework
• Google DeepMind — for the Gemma model family
• HuggingFace TRL — for the SFT training utilities

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Made with ❤️ using Unsloth