Adapter weights only for meta-llama/Meta-Llama-3-8B-Instruct, fine-tuned to answer medical multiple-choice questions (A/B/C/D).
Subjects used for fine-tuning and evaluation: Biochemistry and Physiology.

Educational use only. Not medical advice.

Access note: Llama-3 base is a public gated model on HF.
Accept the base model license on its page and use a fine-grained token that allows public gated repos.

Quick use (Transformers + PEFT)

python

import os, re
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

BASE = "meta-llama/Meta-Llama-3-8B-Instruct"
ADAPTER = "Pk3112/medmcqa-lora-llama3-8b-instruct"
hf_token = os.getenv("HUGGINGFACE_HUB_TOKEN")  # required if not logged in

tok = AutoTokenizer.from_pretrained(BASE, use_fast=True, token=hf_token)
base = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto", token=hf_token)
model = PeftModel.from_pretrained(base, ADAPTER, token=hf_token).eval()

prompt = (
    "Question: Which vitamin is absorbed in the ileum?\n"
    "A. Vitamin D\nB. Vitamin B12\nC. Iron\nD. Fat\n\n"
    "Answer:"
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=8, do_sample=False)
text = tok.decode(out[0], skip_special_tokens=True)

m = re.search(r"Answer:\s*([A-D])\b", text)
print(f"Answer: {m.group(1)}" if m else text.strip())

Tip: For rich explanations, increase max_new_tokens. For answer-only, keep it small and stop after the letter to reduce latency.

Results (Biochemistry + Physiology)

Model	Internal val acc (%)	Original val acc (%)	TTFT (ms)	Gen time (ms)	In/Out tokens
Llama-3-8B (LoRA)	83.83	65.20	567	14874	148 / 80

Training (summary)

Frameworks: Unsloth + PEFT/LoRA (QLoRA NF4)
LoRA: r=32, alpha=64, dropout=0.0; targets q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
Max seq length: 768
Objective: answer-only target (Answer: <A/B/C/D>)
Split: stratified 70/30 on subject_name (Biochemistry, Physiology)

Training code & reproducibility

GitHub repo: https://github.com/PranavKumarAV/MedMCQA-Chatbot-Finetune-Medical-AI
Release (code snapshot): https://github.com/PranavKumarAV/MedMCQA-Chatbot-Finetune-Medical-AI/releases/tag/v1.0-medmcqa

Files provided

adapter_model.safetensors
adapter_config.json

License & usage

Adapter: “Other” — adapter weights only; use requires access to the base model under the Meta Llama 3 Community License (accept on base model page)
Base model: meta-llama/Meta-Llama-3-8B-Instruct (public gated on HF)
Dataset: openlifescienceai/medmcqa — follow dataset license
Safety: Educational use only. Not medical advice.