Fine-tuned Gemma-3-1B for structured clinical symptom extraction from unstructured medical text. Distills GPT-4o clinical NLP capability into a small, deployable model.

Model Description

Base model: google/gemma-3-1b-it
Fine-tuning: LoRA (r=16, alpha=32, q_proj + v_proj)
Task: Clinical symptom extraction → structured JSON
Developed by: Janushi Shastri
License: Apache 2.0

What It Does

Converts unstructured clinical text into structured JSON.

Input: "been feeling off for a few days, chest feels weird and i get tired just walking around"

Output:

json

{
  "symptoms": ["chest discomfort", "fatigue"],
  "duration": ["few days", "unspecified"],
  "severity": ["unspecified", "mild"],
  "urgent": true
}

Input: "stomach's been acting up since yesterday, went to the bathroom like 4 times, feeling drained"

Output:

json

{
  "symptoms": ["diarrhea", "fatigue"],
  "duration": ["since yesterday", "unspecified"],
  "severity": ["unspecified", "mild"],
  "urgent": false
}

Performance

Evaluated on 35 held-out clinical examples:

Metric	Score
Valid JSON rate	100%
Symptom F1	0.781
Urgent Accuracy	85.7%

Cross-Model Benchmark

Model	Method	F1	Urgent Acc
Gemma-3-1B	LoRA	0.781	85.7%
Gemma-3-1B	QLoRA	0.740	82.9%
LLaMA-3.2-1B	LoRA	0.743	74.3%
LLaMA-3.2-1B	QLoRA	0.767	74.3%
Qwen1.5-1.8B	LoRA	0.707	74.3%
Qwen1.5-1.8B	QLoRA	0.696	87.9%

How to Use

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "YOUR_HF_USERNAME/ClinicalDistill-Gemma-1B"

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

def extract_clinical(text):
    prompt = f"""<instruction>
Extract symptoms from the clinical note below. Reply with ONLY valid JSON.
Format: {{"symptoms": ["s1"], "duration": ["d1"], "severity": ["sev1"], "urgent": true/false}}
Use "unspecified" if unknown. urgent=true only for chest pain, breathing difficulty, stroke, severe bleeding.
</instruction>
<input>{text}</input>
<o>"""

    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=200,
            temperature=0.1,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("<o>")[-1].replace("</o>", "").strip()

print(extract_clinical("Patient has chest pain for 3 days and mild fever"))

Training Details

Dataset: 145 synthetic clinical examples (GPT-4o generated)
Domains: Cardiac, respiratory, neurological, gastrointestinal
Epochs: 7
Batch size: 2 (gradient accumulation: 4)
Learning rate: 2e-4
Hardware: Google Colab T4 GPU
Training time: ~8 minutes

Intended Use

Clinical NLP research
Healthcare AI prototyping
Resource-limited deployment (runs on single GPU)

Limitations

Trained on synthetic data — real clinical notes may differ
English only
Best suited for symptom extraction, not diagnosis

Citation

bibtex

@misc{shastri2026clinicaldistill,
  title={Benchmarking Small LLMs for Clinical Symptom Extraction 
         on Resource-Constrained Compute},
  author={Shastri, Janushi},
  year={2026}
}

Janushi/ClinicalDistill-Gemma-1B

ClinicalDistill-Gemma-1B