Gemma-3 Endocrinology Scenario-Based MCQ Models (20 Variants)

This collection contains 20 instruction-tuned Gemma-3 variants (270M–27B) optimized for endocrinology scenario-based MCQs and clinical Q&A. Variants are provided as LoRA adapters, merged FP16, and GGUF builds (FP16 and Q4_K_M) for flexible deployment from research servers to local/offline inference.

Collection link: https://huggingface.co/collections/HAJERI/gemma-3-endocrinology-scenario-based-mcq-models

What the models can do

All variants are designed for three primary inference modes:

Scenario MCQ Answering (with explanation): Provide the single best answer (A/B/C/D) and explain the reasoning.
Scenario MCQ Generation: Generate a full clinical MCQ (stem + options + correct answer + brief explanation).
General Endocrinology Q&A: Provide concise, structured answers to general endocrinology questions.

Important: These models are intended for educational and research purposes only and must not be used for clinical decision-making.

Dataset provenance and medical review

The dataset was designed and prepared by Professor _Saeed Awad M. Alqahtani _ (Taibah University, Saudi Arabia) and reviewed and approved by three licensed medical doctors:

Two consultant endocrinologists
One general medical doctor

Model variants in this collection:

270M (4 variants)

HAJERI/gemma-3-270m-endocrinology-scenario-mcq-it-lora
HAJERI/gemma-3-270m-endocrinology-scenario-mcq-it-merged-fp16
HAJERI/gemma-3-270m-endocrinology-scenario-mcq-it-fp16-gguf
HAJERI/gemma-3-270m-endocrinology-scenario-mcq-it-Q4_K_M-gguf

1B (4 variants)

HAJERI/gemma-3-1b-endocrinology-scenario-mcq-it-lora
HAJERI/gemma-3-1b-endocrinology-scenario-mcq-it-merged-fp16
HAJERI/gemma-3-1b-endocrinology-scenario-mcq-it-fp16-gguf
HAJERI/gemma-3-1b-endocrinology-scenario-mcq-it-Q4_K_M-gguf

4B (4 variants)

HAJERI/gemma-3-4b-endocrinology-scenario-mcq-it-lora
HAJERI/gemma-3-4b-endocrinology-scenario-mcq-it-merged-fp16
HAJERI/gemma-3-4b-endocrinology-scenario-mcq-it-fp16-gguf
HAJERI/gemma-3-4b-endocrinology-scenario-mcq-it-Q4_K_M-gguf

12B (4 variants)

HAJERI/gemma-3-12b-endocrinology-scenario-mcq-it-lora
HAJERI/gemma-3-12b-endocrinology-scenario-mcq-it-merged-fp16
HAJERI/gemma-3-12b-endocrinology-scenario-mcq-it-fp16-gguf
HAJERI/gemma-3-12b-endocrinology-scenario-mcq-it-Q4_K_M-gguf

27B (4 variants)

HAJERI/gemma-3-27b-endocrinology-scenario-mcq-it-lora
HAJERI/gemma-3-27b-endocrinology-scenario-mcq-it-merged-fp16
HAJERI/gemma-3-27b-endocrinology-scenario-mcq-it-fp16-gguf
HAJERI/gemma-3-27b-endocrinology-scenario-mcq-it-Q4_K_M-gguf

How to choose the right variant

Pick by deployment format

-lora: best if you want to keep the base model separate and apply adapters (research workflows, experimentation).
-merged-fp16: best for GPU inference with a standard Transformers pipeline (higher quality, higher VRAM).
-fp16-gguf: best for llama.cpp inference with high quality (larger RAM/VRAM than quantized).
-Q4_K_M-gguf: best for fast, memory-efficient local inference (most users on laptops/desktops).

Pick by model size

270M / 1B: fastest, lowest resource, good for lightweight education and constrained hardware.
4B: strong balance of speed and reasoning.
12B: best general quality/medical reasoning balance for many users.
27B: highest reasoning quality (requires more compute).

Prompt templates (the 3 modes)

Mode 1 — MCQ Answer + Explanation

You are an expert endocrinologist.

Clinical scenario:

A 32-year-old woman presents with weight loss, heat intolerance, and palpitations.

Labs: suppressed TSH and elevated free T4.

Options: A. Subacute thyroiditis B. Graves disease C. Hashimoto thyroiditis D. Pituitary adenoma

Instructions:

Choose the single best answer.
Explain the reasoning.

Mode 2 — Generate a full MCQ

You are an expert endocrinologist.

Create a clinical multiple-choice question on diabetic ketoacidosis suitable for final-year medical students.

Include:

A clinical scenario
Four answer options
The correct answer
A brief explanation

Mode 3 — General Endocrinology Q&A

You are an expert endocrinologist.

Question: Explain the mechanism of action of metformin.

Inference examples

A) GGUF inference with llama.cpp (recommended for GGUF models)

Example (Q4_K_M GGUF):

./main
-m gemma-3-12b-endocrinology-scenario-mcq-it-Q4_K_M.gguf
-p "You are an expert endocrinologist. Explain the pathophysiology of diabetic ketoacidosis."
-n 400
--temp 0.7

Example (FP16 GGUF):

./main
-m gemma-3-12b-endocrinology-scenario-mcq-it-fp16.gguf
-p "You are an expert endocrinologist. Create a clinical MCQ on DKA with 4 options and explanation."
-n 500
--temp 0.7

B) Merged FP16 inference with Transformers (for -merged-fp16)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "HAJERI/gemma-3-12b-endocrinology-scenario-mcq-it-merged-fp16"

tok = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")

prompt = """You are an expert endocrinologist.

Question: Explain the mechanism of action of metformin. """ inputs = tok(prompt, return_tensors="pt") out = model.generate(**inputs, max_new_tokens=350, temperature=0.7, do_sample=True)

print(tok.decode(out[0], skip_special_tokens=True))

Safety note

These models can generate clinically plausible text. Always apply:

Human expert review
Educational context only
No patient-specific decisions

Compute acknowledgement

Compute resources (NVIDIA A100 80GB) were supplied by the National eLearning Center (NeLC), Saudi Arabia, as part of the AI Sandbox in Digital Learning initiative.