Med-LLaMA3.1-8B — Medical (merged, standalone)

A full, ready-to-use medical model: Llama-3.1-8B 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 8B (high-capacity flagship) 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 (4.01% for this 8B variant), achieving strong medical question-answering performance while keeping the memory footprint low — enabling development and inference on low-cost, consumer-grade hardware.

The 8B variant is the high-capacity model for complex clinical reasoning. It attains a mean accuracy of 75.71% across the eight MMLU medical subsets and performs comparably to the institutionally trained LLaMA3-Med42-8B at the same scale, while being trained on consumer-grade GPUs.

• 📄 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-3.1-8B-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-3.1-8B-Instruct yourself, use the adapter repo. Both produce identical outputs. Note: the 8B uses LLaMA 3.1, not 3.2.

Intended uses

Primary use cases

• Medical question answering (multiple-choice and open-ended) — the highest-accuracy variant in the family.
• Clinical knowledge lookup and clinical decision support assistance.
• Clinical named-entity recognition (Disease / Procedure extraction) — see results below.
• Clinical documentation assistance and literature synthesis (with physician review).
• A research baseline for parameter-efficient fine-tuning of 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-3.1-8B-Instruct-Medical-Finetuned-merged"

pipe = pipeline("text-generation", model=MODEL, torch_dtype="auto", 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-3.1-8B-Instruct-Medical-Finetuned-merged"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype="auto", 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 (recommended; ~5 GB GPU memory)

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

MODEL = "MohamedAhmedAE/Llama-3.1-8B-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 8B variant.

Evaluation

All re-run models (including the baselines) were evaluated with the EleutherAI LM Evaluation Harness under identical conditions: same harness version (v0.4.2), identical prompt templates, and 5-shot prompting. Reported ± values are 95% bootstrap confidence intervals (1000 resamples); statistical significance uses McNemar’s test on per-item paired correctness. The merged model is functionally identical to the base + adapter, so all scores below apply to both.

MMLU medical subsets (5-shot accuracy %)

Comparison against the institutionally trained LLaMA3-Med42-8B under identical conditions (this is the comparison visualized in Figure 3 of the paper). Family context: mean accuracy scales with size — 1B = 48.64%, 3B = 64.24%, 8B = 75.71%.

Statistical interpretation (honest framing):

• vs. Llama-3.1-8B-Instruct (untuned base): improvements are statistically significant on Anatomy (p=0.021), Clinical Knowledge (p=0.003), Medical Genetics (p<0.001), Nutrition (p<0.001), and Professional Medicine (p=0.008); College Biology (p=0.11) and College Medicine (p=0.43) are not significant. After Bonferroni correction across the eight subsets (per-subset threshold 0.00625), Clinical Knowledge, Medical Genetics, and Nutrition remain significant; Anatomy and Professional Medicine are significant only before correction.
• vs. LLaMA3-Med42-8B (institutionally trained): differences are not statistically significant — i.e., comparable performance, not demonstrated superiority, at the same parameter scale.

Medical QA benchmarks (5-shot accuracy %)

The +2.2-point gain on MedMCQA over Llama-3.1-8B-Instruct is statistically significant (p=0.004). The difference vs. LLaMA3-Med42-8B on MedMCQA is not significant (p=0.38). On MedQA and PubMedQA the models are statistically tied.

Clinical named-entity recognition (zero-shot, 100 MIMIC-III discharge summaries)

Dual-annotator gold standard, inter-rater agreement κ = 0.87.

Efficiency (4-bit inference)

Approximately 3.58 GB model size and ~5.2 GB GPU memory allocated at inference, ~30 tokens/s, with ~20 ms first-token latency — comparable to other 8B 4-bit baselines and well within consumer-GPU budgets.

See the paper for full tables, generation-quality metrics, expert evaluation, and the safety analysis.

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. In expert review of 100 generative cases, 94% were rated “Good” but 6% contained critical errors — underscoring the need for human verification.
• Comparable, not superior. Against the institutionally trained LLaMA3-Med42-8B, differences are not statistically significant; gains over the untuned base are significant on some subsets but not all (see above). On MedQA and PubMedQA there is no significant advantage.
• Hallucination & over-elaboration. The model can produce fluent but incorrect information and tends to elaborate beyond the question, which may obscure key points in time-sensitive settings.
• Abbreviation ambiguity. A known high-severity error source. The paper’s safety pilot shows that context-disambiguation preprocessing reduces abbreviation-ambiguity 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. Rare-condition coverage is limited.
• Privacy & compliance. Do not input protected health information (PHI) unless your deployment is appropriately secured and compliant with applicable regulations (e.g., HIPAA, GDPR).
• Evaluation scope. Benchmarks are English-only and dominated by multiple-choice formats; long-form reasoning, multi-turn dialogue safety, non-English text, and out-of-distribution robustness are not evaluated.

Recommended deployment: clinical decision support (not autonomous decisions), educational tool, documentation assistant (with physician review), and literature synthesis.

License

This model is released under the Llama 3.1 Community License, inherited from the base model. By using it you agree to Meta’s Llama 3.1 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