medgemma-soap-finetuned1

MedGemma SOAP Note Generation Model

Overview

This model is a fine-tuned variant of MedGemma (Google's medical-domain Gemma 3 model family), adapted to draft clinical SOAP notes (Subjective, Objective, Assessment, Plan) from consultation text. It was built on top of unsloth/medgemma-4b-it-unsloth-bnb-4bit, a 4-bit quantized build of google/medgemma-4b-it, and fine-tuned with Unsloth and Hugging Face's TRL library. The goal is to help reduce the clinical documentation burden by structuring free-text encounter notes or transcripts into a standard SOAP format — as a research/portfolio project, not a deployed clinical tool.

Training Details

The values below are as reported by the model author in the original repository card. No trainer_state.json, evaluation logs, or adapter_config.json are included in the repository, so these figures could not be independently re-derived from raw training artifacts — they are presented here as author-reported metadata rather than independently verified benchmarks.

This repository hosts the fully merged model weights (model-00001-of-00002.safetensors, model-00002-of-00002.safetensors) rather than a standalone LoRA adapter — the QLoRA weights described above were merged back into the base model before upload.

Intended Use

• Drafting SOAP-formatted clinical notes from consultation text/transcripts, as a starting point for a clinician to review and edit.
• Assisting with structuring unorganized clinical text into Subjective, Objective, Assessment, and Plan sections.
• Educational and portfolio demonstration of applying parameter-efficient fine-tuning (QLoRA via Unsloth) to a medical-domain LLM.

This is a research/portfolio project and has not been evaluated or certified as a medical device. It is not intended for direct use in patient care without human clinical oversight, further validation, and regulatory review.

How to Use

The model uses the standard Gemma 3 chat template (<start_of_turn>user ... <end_of_turn>). Load it with transformers as follows:

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "Ephraimmm/medgemma-soap-finetuned1"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": (
                "Convert the following clinical consultation notes into a SOAP note "
                "(Subjective, Objective, Assessment, Plan):\n\n"
                "Patient reports a 3-day history of sore throat and low-grade fever. "
                "Temp 37.9C, throat erythematous, no exudate. Rapid strep negative. "
                "Plan: supportive care, follow up if symptoms worsen."
            )}
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=512)

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

Note: the base architecture (Gemma3ForConditionalGeneration) supports multimodal image+text input, but this fine-tune was trained and is intended for text-only SOAP note generation.

Limitations

• Not clinically validated. No formal clinical accuracy, safety, or bias evaluation has been performed or is included in this repository. Any accuracy or quality figures beyond the training/eval loss values above should not be assumed.
• Not a substitute for professional medical judgment. Output must be reviewed and verified by a qualified clinician before any use in real patient documentation or care.
• No independent training-run artifacts. Since trainer_state.json and adapter configuration files are not present in this repository, the exact LoRA/training hyperparameters could not be independently confirmed beyond what the author documented.
• Domain and demographic coverage unknown. The fine-tuning dataset's size, sourcing, and representativeness are not documented in this repository, so generalization to arbitrary clinical specialties, populations, or documentation styles is unverified.
• Hallucination risk. As with any generative LLM, the model may produce plausible-sounding but incorrect or fabricated clinical content (e.g., inventing findings not present in the source text).
• Should not be used for real patient care, billing, or legal documentation without proper clinical validation, human review, and appropriate regulatory approval.

Author

Developed by Ephraimmm