Med-LLaMA3.2-3B — Medical (merged, standalone)

A full, ready-to-use medical model: Llama-3.2-3B adapted to the medical domain with QLoRA, with the LoRA weights merged back into the base. Load it directly with transformers — no adapter, no PEFT, no extra steps. For the lightweight LoRA-adapter version (apply on top of the base yourself), see the link 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 keeping the memory footprint low — 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
• 🧩 LoRA-adapter version: MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned

Model details

Adapter vs. merged. This repo is the merged model — the medical LoRA is already fused into the weights, so you load it like any standard causal-LM. If you instead want the small (~MB) adapter to apply on top of meta-llama/Llama-3.2-3B-Instruct yourself, use the adapter repo. Both produce identical outputs.

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 standalone model — load it directly, no adapter step required.

pip install -U transformers accelerate torch

Quick start (pipeline)

import torch
from transformers import pipeline

MODEL = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged"

pipe = pipeline("text-generation", model=MODEL, torch_dtype=torch.bfloat16, device_map="auto")

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?"},
]
out = pipe(messages, max_new_tokens=256, do_sample=False)
print(out[0]["generated_text"][-1]["content"])

Full control (AutoModelForCausalLM)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged"

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

messages = [
    {"role": "system", "content": "You are a knowledgeable medical assistant. Answer accurately and concisely."},
    {"role": "user", "content": "Explain the mechanism of action of metformin."},
]
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))

Low-memory 4-bit inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# pip install -U bitsandbytes

MODEL = "MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged"

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(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, quantization_config=bnb_config, device_map="auto")

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

This model was produced by QLoRA fine-tuning followed by merging the adapter into the base. 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.

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%.

The merged model is functionally identical to the base + adapter, so these scores apply to both. 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 model 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:

@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

Fine-tuning dataset: MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset