This is a LoRA adapter trained on top of NVIDIA's Nemotron-3-Nano-30B model using the MedMCQA dataset. The adapter specializes the base model for answering medical multiple-choice questions, covering topics from AIIMS and NEET PG medical entrance exams.

Base Model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16
Model Type: LoRA Adapter (PEFT)
Language: English
License: NVIDIA Nemotron Open Model License
Fine-tuned from: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16

Model Sources

Base Model: NVIDIA Nemotron-3-Nano-30B
Training Dataset: openlifescienceai/medmcqa

Uses

Direct Use

This adapter is designed for medical question answering tasks, particularly multiple-choice questions in the medical domain. It can be used for:

Answering medical knowledge questions
Educational medical Q&A systems
Medical exam preparation assistance

Out-of-Scope Use

This model should NOT be used for:

Clinical diagnosis or medical decision-making
Replacing professional medical advice
Patient care or treatment recommendations
Any use case where errors could cause harm to individuals

Bias, Risks, and Limitations

Not a Medical Device: This model is not FDA-approved or validated for clinical use
Training Data Bias: The model is trained on Indian medical entrance exam data (AIIMS/NEET PG), which may not generalize to all medical contexts
No Guarantee of Accuracy: The model can and will make mistakes on medical questions
English Only: The model is trained on English medical content only

Recommendations

Always verify medical information with qualified healthcare professionals
Use this model for educational or research purposes only
Do not rely on this model for any medical decisions

How to Get Started with the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model and tokenizer
base_model_id = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16"
adapter_id = "YOUR_USERNAME/nemotron-nano-medmcqa-lora"  # Update with your repo

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype="auto",
    device_map="auto",
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, adapter_id)

# Example usage
prompt = """<|im_start|>system
You are a medical expert. Answer the multiple choice question.<|im_end|}
<|im_start|>user
Question: Which drug causes cinchonism?

A) Aspirin
B) Quinine
C) Paracetamol
D) Ibuprofen<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on the MedMCQA dataset, which contains:

194k+ multiple-choice questions from Indian medical entrance exams
Questions from AIIMS and NEET PG exams
Coverage of 2,400+ healthcare topics across 21 medical subjects
Includes explanations for correct answers

Training Procedure

Training Hyperparameters

Parameter	Value
LoRA Rank (r)	16
LoRA Alpha	32
LoRA Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj, up_proj, down_proj
Max Sequence Length	1024
Training Precision	BF16
Optimizer	Unsloth optimized

LoRA Configuration

json

{
  "r": 16,
  "lora_alpha": 32,
  "lora_dropout": 0.05,
  "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "down_proj"],
  "task_type": "CAUSAL_LM",
  "bias": "none"
}

Technical Specifications

Compute Infrastructure

Training Framework: Unsloth + TRL (SFTTrainer)
PEFT Version: 0.18.1

License

This model is released under the NVIDIA Nemotron Open Model License.

Key terms:

Commercial use: Permitted
Derivative works: Permitted
Distribution: Permitted with attribution

Citation

If you use this adapter, please cite the base model and dataset:

bibtex

@misc{nemotron3nano,
  title={NVIDIA Nemotron-3-Nano-30B},
  author={NVIDIA},
  year={2025},
  url={https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16}
}

@article{pal2022medmcqa,
  title={MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
  author={Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
  journal={arXiv preprint arXiv:2203.14371},
  year={2022}
}

Framework Versions

PEFT: 0.18.1
Transformers: Compatible with transformers 4.x
Unsloth: Used for training optimization

Framework versions

PEFT 0.18.1

sara4dev/unsloth-nemotron-3-nano-medical-qa-lora

Nemotron-Nano MedMCQA LoRA Adapter

Model Details

Model Description