SOAP_SFT_V1 — Medical SOAP Note Generator

SOAP_SFT_V1 is a fine-tuned version of Gemma 3 4B Instruct, trained to generate structured clinical SOAP notes (Subjective, Objective, Assessment, Plan) from doctor–patient dialogues.

Trained 2x faster with Unsloth and Hugging Face's TRL library on an H100 GPU.

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Model Details

Property	Value
Developed by	Edifon
Base model	unsloth/gemma-3-4b-it-unsloth-bnb-4bit
Model type	Causal Language Model (fine-tuned)
Language	English
License	Apache 2.0
Fine-tuning method	Supervised Fine-Tuning (SFT) with LoRA
Training hardware	Google Colab H100

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Intended Use

This model is designed to assist healthcare professionals and clinical NLP researchers by automatically converting clinical consultation transcripts into structured SOAP notes.

SOAP format:

• S (Subjective): Patient-reported symptoms, history, and complaints
• O (Objective): Observable/measurable clinical findings and planned investigations
• A (Assessment): Differential diagnosis and clinical reasoning
• P (Plan): Treatment plan, referrals, and follow-up instructions

⚠️ Disclaimer: This model is intended as a research and assistive tool only. It is not a substitute for professional medical judgment or a licensed clinician's evaluation.

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Training Details

Dataset

• Dataset: syafiqassegaf/soap-dataset (Kaggle)
• Total examples: 9,250
• Train / Eval split: 90% / 10% → 8,325 train | 925 eval
• Features: dialogue, soap, prompt, messages

LoRA Configuration

Parameter	Value
Rank (r)	8
Alpha (lora_alpha)	8
Dropout	0
Bias	none
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable parameters	16,394,240 / 4,316,473,712 (0.38%)
Vision layers finetuned	No
Language layers finetuned	Yes

Training Hyperparameters

Parameter	Value
Epochs	5
Per-device batch size	2
Gradient accumulation steps	4 (effective batch size = 8)
Learning rate	2e-5
LR scheduler	Linear
Optimizer	AdamW 8-bit
Weight decay	0.001
Warmup steps	5
Max sequence length	2048
Seed	3407
Total steps	5,205

Training used train_on_responses_only — only model responses were used in the loss computation, not the user instructions.

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How to Use

With transformers (Standard)

from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Edifon/SOAP_SFT_V1")
model = AutoModelForImageTextToText.from_pretrained("Edifon/SOAP_SFT_V1", device_map="auto")

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": (
            "You are an expert medical professor assisting in the creation of medically accurate SOAP summaries. "
            "Please ensure the response follows the structured format: S:, O:, A:, P: without using markdown or special formatting."
        )}],
    },
    {
        "role": "user",
        "content": [{"type": "text", "text": """Create a medical SOAP summary of this dialogue.

### Dialogue:
Doctor: Hello, what brings you in today?
Patient: I've been having severe headaches for the past few weeks...
[rest of dialogue]
"""}],
    },
]

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

from transformers import TextStreamer
_ = model.generate(
    **inputs,
    max_new_tokens=2048,
    streamer=TextStreamer(processor, skip_prompt=True),
)

With Unsloth (Faster Inference)

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="Edifon/SOAP_SFT_V1",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastModel.for_inference(model)

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Example Output

Input dialogue (excerpt):

Patient reports photopsia in the left eye for ten days, including flashes of light and a dark spot on the nasal side. Had influenza-like symptoms two weeks prior. No history of eye disease.

Model output:

S: Patient reports experiencing photopsia in the left eye for ten days, describing flashes of light
   and a dark spot on the nasal side. History of influenza-like symptoms two weeks prior.
   No prior eye disease, operations, or treatments.

O: Patient presented with photopsia and a dark spot in the left eye. Comprehensive eye examination
   planned (visual acuity, slit-lamp, fundus examination).

A: Differential includes post-infectious transient optic neuropathy or acute ocular involvement
   secondary to influenza. Absence of prior eye disease supports opportunistic onset.

P: Order comprehensive eye examination. Schedule follow-up to review results and determine
   treatment or referral plan. Encourage prompt completion of planned examination.

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Training Curve

Metric	Value
Initial loss (step 100)	0.941
Final loss (step 5200)	0.482
Total reduction	~48.8%

The model converged stably over 5 epochs / 5,205 steps. Loss dropped sharply in the first ~300 steps as the model learned the SOAP output format, then decayed gradually through step ~2,000, before plateauing in the 0.48–0.52 range for the final two epochs with no significant overfitting observed.

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Limitations

• Trained exclusively on English-language dialogues
• Performance may degrade on highly specialized subspecialty consultations underrepresented in the training data
• Should not be used for clinical decision-making without expert oversight
• Outputs may occasionally include disclaimers or formatting inconsistencies

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Citation

If you use this model in your research, please cite the base model and dataset:

@misc{soap_sft_v1,
  author       = {Edifon},
  title        = {SOAP\_SFT\_V1: Medical SOAP Note Generator},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Edifon/SOAP_SFT_V1}
}