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Llama-3.2-1B-Instruct-Medical-Finetuned

Text Generation
PEFT
Safetensors
English
medical
healthcare
clinical
clinical-decision-support
question-answering
medical-qa
llama
llama-3.2
lora
qlora
parameter-efficient-fine-tuning
4-bit precision
edge
conversational
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metadata 
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
pipeline_tag: text-generation
language:
  - en
tags:
  - medical
  - healthcare
  - clinical
  - clinical-decision-support
  - question-answering
  - medical-qa
  - llama
  - llama-3.2
  - lora
  - qlora
  - peft
  - parameter-efficient-fine-tuning
  - 4-bit
  - edge
datasets:
  - medalpaca/medical_meadow_medqa
  - medalpaca/medical_meadow_medical_flashcards
  - medalpaca/medical_meadow_wikidoc
  - medalpaca/medical_meadow_wikidoc_patient_information
  - medalpaca/medical_meadow_cord19
  - medalpaca/medical_meadow_pubmed_causal
  - openlifescienceai/medmcqa
  - bigbio/med_qa
  - qiaojin/PubMedQA
  - deepset/covid_qa_deepset

Med-LLaMA3.2-1B — Medical QLoRA Adapter (LoRA weights only)

Parameter-efficient medical adaptation of Llama-3.2-1B 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 1B (lightweight / edge) 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 (6.80% for this 1B 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 1B variant is designed for edge deployment and resource-constrained, on-device use cases where footprint and latency matter most.

Model details

Base model meta-llama/Llama-3.2-1B-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 90.17 M LoRA parameters = 6.80% of the 1.32 B total (base weights frozen)
Compute dtype bfloat16 (mixed precision)
Architecture 16 decoder layers · hidden size 2048 · intermediate size 8192 · GQA (32 attention 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-3.2-1B-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.
  • On-device / edge medical NLP where a small footprint is required.
  • 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 (recommended for the 1B edge use case) 

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

BASE_MODEL = "meta-llama/Llama-3.2-1B-Instruct"
ADAPTER    = "MohamedAhmedAE/Llama-3.2-1B-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 

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

BASE_MODEL = "meta-llama/Llama-3.2-1B-Instruct"
ADAPTER    = "MohamedAhmedAE/Llama-3.2-1B-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:

merged = model.merge_and_unload()
merged.save_pretrained("Med-LLaMA3.2-1B-Medical-merged")
tokenizer.save_pretrained("Med-LLaMA3.2-1B-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 1B variant.

Setting Value (1B)
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 90.17 M (6.80% of 1.32 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 1024
Batch size / grad accumulation 10 per device / 40 steps
Max gradient norm 1.0
Precision & memory bfloat16 · gradient checkpointing · DeepSpeed ZeRO-2 · FlashAttention-2
Hardware 2 × NVIDIA RTX 4050 (12 GB), ~23 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 1B 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-LLaMA3.2-1B (acc. %)
Anatomy 47.41 (±4.31)
Clinical Knowledge 48.30 (±3.08)
College Biology 46.53 (±4.17)
College Medicine 38.15 (±3.70)
Medical Genetics 52.00 (±5.02)
Nutrition 59.15 (±2.81)
Professional Medicine 56.62 (±3.01)
Virology 40.96 (±3.83)
Mean (8 subsets) 48.64

The paper reports an untuned baseline only for the 8B model (vs. Llama-3.1-8B-Instruct); it does not include an untuned Llama-3.2-1B baseline on these subsets. See Table 7 of the paper for the full cross-model comparison (3B, 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.
  • Smallest variant. As the 1B model, it has the lowest capacity in the family and is more prone to errors on complex clinical reasoning than the 3B and 8B variants. Prefer larger variants when accuracy is critical and resources allow.
  • 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:

@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 this repo — 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 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