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MohamedAhmedAE/
Llama-3.2-3B-Instruct-Medical-Finetuned

Name: Llama-3.2-3B-Instruct-Medical-Finetuned
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Med-LLaMA3.2-3B — Medical QLoRA Adapter (LoRA weights only)

Parameter-efficient medical adaptation of Llama-3.2-3B using QLoRA (4-bit NF4 + LoRA). This repository contains the LoRA adapter only — it must be applied on top of the base model at load time. For a ready-to-use, standalone checkpoint, see the merged version linked below.

This is the 3B (balanced / mid-tier) member of the Med-LLaMA3 family introduced in the paper “Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models” (Applied Sciences, 2026). The family adapts the LLaMA-3 architecture to the medical domain by training only a small fraction of the base model’s parameters (5.70% for this 3B variant), achieving strong medical question-answering performance while reducing memory use by roughly 75% via 4-bit quantization — enabling development and inference on low-cost, consumer-grade hardware.

The 3B variant offers a balanced trade-off between computational efficiency and capacity — a competitive mid-tier option with meaningfully better accuracy than the 1B and lower compute than the 8B.

📄 Paper: Med-LLaMA3 (Applied Sciences 2026, 16(12), 6158) · DOI: 10.3390/app16126158
💻 Code: github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers
🧩 Merged (standalone) version: MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged

Model details


Base model	`meta-llama/Llama-3.2-3B-Instruct`
Adaptation method	QLoRA — 4-bit NF4 quantization (double quantization) + LoRA
LoRA rank (`r`)	128
LoRA alpha (`α`)	256 (scaling `α/r = 2.0`)
LoRA target modules	All linear layers — `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Trainable parameters	194.51 M LoRA parameters = 5.70% of the 3.40 B total (base weights frozen)
Compute dtype	bfloat16 (mixed precision)
Architecture	28 decoder layers · hidden size 3072 · intermediate size 8192 · GQA (24 query heads, 8 KV heads)
Context window	128K tokens (inherited from base)
Vocabulary	128,256 tokens
Language	English
License	Llama 3.2 Community License

ℹ️ Base checkpoint. A LoRA adapter only loads correctly onto the exact base model it was trained on. This adapter targets the instruct checkpoint meta-llama/Llama-3.2-3B-Instruct (consistent with the released fine-tuned model). Use that same base in the code below.

Intended uses

Primary use cases

Medical question answering (multiple-choice and open-ended).
Clinical knowledge lookup and clinical decision support assistance.
A balanced mid-tier option when the 1B is too small and the 8B is too heavy.
A research baseline for parameter-efficient fine-tuning of small LLaMA models in healthcare.

Out of scope / not intended for

Autonomous clinical decision-making or direct patient care without a qualified clinician in the loop.
Generating definitive diagnoses, prescriptions, or treatment plans.
Use as a substitute for professional medical advice, emergency services, or licensed care.

See Limitations & responsible use before any applied use.

How to use

This is a PEFT/LoRA adapter, so you load the base model first and then attach the adapter.

pip install -U transformers peft accelerate bitsandbytes torch

Option A — 4-bit inference (lower GPU memory)

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

BASE_MODEL = "meta-llama/Llama-3.2-3B-Instruct"
ADAPTER    = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned"  # <-- this adapter repo

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb_config,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()

messages = [
    {"role": "system", "content": "You are a knowledgeable medical assistant. Answer accurately and concisely."},
    {"role": "user", "content": "What is the first-line treatment for uncomplicated community-acquired pneumonia in a healthy adult?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(inputs, max_new_tokens=256, do_sample=False, temperature=0.0)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Option B — full-precision inference

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_MODEL = "meta-llama/Llama-3.2-3B-Instruct"
ADAPTER    = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base, ADAPTER)

Optional — merge the adapter into the base

If you want a single standalone model (no PEFT dependency at inference), merge the weights:

python

merged = model.merge_and_unload()
merged.save_pretrained("Med-LLaMA3.2-3B-Medical-merged")
tokenizer.save_pretrained("Med-LLaMA3.2-3B-Medical-merged")

(A pre-merged checkpoint is also published separately — see the link at the top of this card.)

Training data

The Med-LLaMA3 family was fine-tuned on a curated medical instruction dataset of over 1.5 million samples, organized along a three-axis taxonomy: source type (examination QA, clinical dialogue, biomedical literature, encyclopedic reference) × clinical granularity (basic science, clinical reasoning, patient communication) × task format (multiple-choice, open-ended QA, generative dialogue). All sources were consolidated into a unified instruction–response schema (system, context, question, answer, choices).

Sources include:

MedAlpaca / Medical Meadow collection — MEDIQA, Medical Flashcards, WikiDoc, WikiDoc Patient Information, MedQA, CORD-19, and PubMed Causal subsets
MedMCQA — Indian medical entrance exam (AIIMS & NEET PG) multiple-choice questions
MedQA-USMLE — USMLE-style 4-option multiple-choice questions (English)
BigBIO MedQA — standardized biomedical QA
PubMedQA — research questions over PubMed abstracts (yes/no/maybe)
COVID-QA (deepset) — COVID-19 / SARS-CoV-2 question answering
MedQuAD — consumer-health QA compiled from authoritative NIH sources
HealthCareMagic — real-world patient–doctor conversation transcripts

The data-cleaning and corpus-assembly scripts are released in the code repository, and the final compiled fine-tuning dataset is available at MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset.

Evaluation integrity: The eight MMLU medical subsets were used only for held-out evaluation and were excluded from the fine-tuning corpus. For benchmarks with official splits (MedMCQA, MedQA-USMLE, PubMedQA), only the official training partitions were used for fine-tuning.

Training procedure

LoRA and optimization settings are identical across the 1B, 3B, and 8B variants; sequence length, batch size, and gradient accumulation are scaled to each model’s memory footprint. The settings below are for the 3B variant.

Setting	Value (3B)
Method	QLoRA (4-bit NF4 base, LoRA adapters in higher precision)
LoRA `r` / `α` / dropout / bias	128 / 256 / 0.05 / none
Target modules	All linear layers (q, k, v, o, gate, up, down)
Trainable params	194.51 M (5.70% of 3.40 B)
Quantization	4-bit NF4 with double quantization (bitsandbytes)
Optimizer	Paged AdamW 8-bit (β₁ = 0.9, β₂ = 0.999), weight decay 0.1
Learning rate / schedule	2.0 × 10⁻⁵ / cosine annealing, 5 warmup steps
Epochs	5
Max sequence length	2048
Batch size / grad accumulation	4 per device / 16 steps
Max gradient norm	1.0
Precision & memory	bfloat16 · gradient checkpointing · DeepSpeed ZeRO-2 · FlashAttention-2
Hardware	2 × NVIDIA RTX 4050 (12 GB), ~30 days
Experiment tracking	Weights & Biases

The QLoRA recipe keeps the base weights frozen and quantized, allocating optimizer state only for the LoRA parameters — which is what makes fine-tuning feasible on consumer hardware.

Evaluation

Evaluation in the paper uses the EleutherAI LM Evaluation Harness with 5-shot prompting on the eight MMLU medical subsets (Anatomy, Clinical Knowledge, College Biology, College Medicine, Medical Genetics, Nutrition, Professional Medicine, Virology). Reported comparisons include McNemar’s test p-values and 95% bootstrap confidence intervals.

The table below reports the 3B model’s 5-shot accuracy (%) on each MMLU medical subset, with 95% bootstrap confidence intervals (1000 resamples), as published in Table 7 of the paper. For context, the family’s mean accuracy scales with model size: 1B = 48.64%, 3B = 64.24%, 8B = 75.71%.

MMLU medical subset (5-shot)	Med-LLaMA3.2-3B (acc. %)
Anatomy	59.52 (±4.26)
Clinical Knowledge	68.17 (±2.89)
College Biology	71.53 (±3.77)
College Medicine	57.65 (±3.78)
Medical Genetics	75.00 (±4.35)
Nutrition	68.32 (±2.69)
Professional Medicine	70.59 (±2.77)
Virology	43.17 (±3.84)
Mean (8 subsets)	64.24

The paper reports an untuned baseline only for the 8B model (vs. Llama-3.1-8B-Instruct); it does not include an untuned Llama-3.2-3B baseline on these subsets. See Table 7 of the paper for the full cross-model comparison (1B, 8B, and other ≤8B models) with statistical tests.

See the paper for full tables, statistical tests, and confidence intervals.

Limitations & responsible use

Not a medical device. This model is a research artifact. It must not be used for autonomous diagnosis, treatment, prescribing, or any decision affecting patient care without review by a qualified healthcare professional.
Hallucination risk. Like all LLMs, it can produce fluent but incorrect or fabricated medical information. Always verify outputs against authoritative sources.
Mid-tier capacity. The 3B is a balanced variant; for the highest accuracy on complex clinical reasoning, prefer the 8B variant when resources allow. For the smallest footprint, the 1B is available.
Abbreviation ambiguity. Medical abbreviations are a known error source. The paper’s safety pilot shows that context-disambiguation preprocessing reduces the highest-severity abbreviation errors (from 30% to 10% on a held-out set); consider applying similar preprocessing.
Data & bias. Training data may under-represent certain populations, conditions, or regional practices, and may encode biases present in the source corpora.
Privacy & compliance. Do not input protected health information (PHI) unless your deployment is appropriately secured and compliant with applicable regulations (e.g., HIPAA, GDPR).
English only. Performance outside English is not evaluated.

License

This adapter is released under the Llama 3.2 Community License, inherited from the base model. By using it you agree to Meta’s Llama 3.2 license terms and Acceptable Use Policy. Review the licenses of the individual training datasets for any additional restrictions on derived use.

Citation

If you use this model, please cite the paper:

bibtex

@article{aboelenen2026medllama3,
  title   = {Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models},
  author  = {Abo El-Enen, Mohamed Ahmed and Ismail, Sally S. and Nazmy, Taymoor Mohamed},
  journal = {Applied Sciences},
  volume  = {16},
  number  = {12},
  pages   = {6158},
  year    = {2026},
  publisher = {MDPI},
  doi     = {10.3390/app16126158},
  url     = {https://www.mdpi.com/2076-3417/16/12/6158}
}

Authors & contact

Mohamed Ahmed Abo El-Enen, Sally S. Ismail, and Taymoor Mohamed Nazmy Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Model family

Variant	Type	Repository
Med-LLaMA3.2-1B	Adapter	`MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned`
Med-LLaMA3.2-1B	Merged	`MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned-merged`
Med-LLaMA3.2-3B	Adapter	this repo — `MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned`
Med-LLaMA3.2-3B	Merged	`MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged`
Med-LLaMA3.1-8B	Adapter	`MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned`
Med-LLaMA3.1-8B	Merged	`MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged`

Fine-tuning dataset: MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset