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MohamedAhmedAE/
Llama-3.1-8B-Instruct-Medical-Finetuned

Name: Llama-3.1-8B-Instruct-Medical-Finetuned
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Med-LLaMA3.1-8B — Medical QLoRA Adapter (LoRA weights only)

Parameter-efficient medical adaptation of Llama-3.1-8B 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 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 reducing memory use by roughly 75% via 4-bit quantization — 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
🧩 Merged (standalone) version: MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged

Model details


Base model	`meta-llama/Llama-3.1-8B-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	335.54 M LoRA parameters = 4.01% of the 8.36 B total (base weights frozen)
Compute dtype (training)	float16 (mixed precision)
Architecture	32 decoder layers · hidden size 4096 · intermediate size 14,336 · GQA (32 query heads, 8 KV heads)
Context window	128K tokens (inherited from base)
Vocabulary	128,256 tokens
Language	English
License	Llama 3.1 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-3.1-8B-Instruct (note: the 8B uses LLaMA 3.1, not 3.2). Use that same base in the code below.

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 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 (recommended; ~5 GB GPU memory)

python

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

BASE_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
ADAPTER    = "MohamedAhmedAE/Llama-3.1-8B-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-3.1-8B-Instruct"
ADAPTER    = "MohamedAhmedAE/Llama-3.1-8B-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-LLaMA3.1-8B-Medical-merged")
tokenizer.save_pretrained("Med-LLaMA3.1-8B-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 8B variant.

Setting	Value (8B)
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	335.54 M (4.01% of 8.36 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	4096
Batch size / grad accumulation	1 per device / 4 steps
Max gradient norm	1.0
Precision & memory	float16 · gradient checkpointing (no DeepSpeed / FlashAttention — unsupported on T4)
Hardware	2 × NVIDIA T4 (30 GB total, free-tier Kaggle); ~15–25 days per run
Experiment tracking	Weights & Biases

The QLoRA recipe keeps the base weights frozen and quantized, allocating optimizer state only for the 335 M LoRA parameters rather than the 8 B base — which is what makes fine-tuning feasible on a 30 GB 2× T4 setup.

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.

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%.

MMLU medical subset	Med-LLaMA3.1-8B (Ours)	LLaMA3-Med42-8B
Anatomy	71.11 (±3.92)	69.63 (±3.97)
Clinical Knowledge	79.62 (±2.48)	76.60 (±2.61)
College Biology	84.03 (±3.06)	81.25 (±3.26)
College Medicine	67.63 (±3.57)	67.05 (±3.58)
Medical Genetics	84.00 (±3.68)	76.00 (±4.29)
Nutrition	83.66 (±2.12)	72.88 (±2.55)
Professional Medicine	77.21 (±2.55)	75.00 (±2.63)
Virology	58.43 (±3.84)	49.40 (±3.89)
Mean (8 subsets)	75.71	71.10

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 %)

Model	MedMCQA	MedQA	PubMedQA
Med-LLaMA3.1-8B (Ours)	61.8 (±0.75)	62.4 (±1.37)	77.0 (±1.96)
LLaMA3-Med42-8B	60.3 (±0.76)	62.8 (±1.36)	77.0 (±1.88)
Llama-3.1-8B-Instruct	59.6 (±0.76)	61.9 (±1.36)	75.8 (±1.92)
MedGemma-4B-it	32.2 (±0.72)	27.7 (±1.26)	55.2 (±2.23)

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.

Entity type	Precision	Recall	F1	Support
Disease	0.94	0.91	0.92	847
Procedure	0.97	0.93	0.95	463
Macro avg	0.96	0.92	0.94	1310

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 adapter 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:

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	`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	this repo — `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