---
language: en
license: mit
base_model: Qwen/Qwen3-1.7B
tags:
  - medical
  - lora
  - peft
  - rocm
  - amd
  - question-answering
  - fine-tuned
datasets:
  - openlifescienceai/medmcqa
---

# MedQA — Qwen3-1.7B LoRA Fine-tuned on MedMCQA

Clinical question-answering AI fine-tuned on MedMCQA.
Built on AMD Instinct MI300X via ROCm — no CUDA required.

## Model Details

- Base Model  : Qwen/Qwen3-1.7B
- Fine-tuning : LoRA (r=4, target: q_proj + v_proj)
- Dataset     : openlifescienceai/medmcqa (193k clinical MCQs)
- Hardware    : AMD Instinct MI300X (192GB HBM3)
- Precision   : bfloat16 (ROCm native)
- Trainable   : ~2.2M of 1.7B parameters (0.15%)

## What It Does

Given a clinical multiple-choice question with 4 options,
the model selects the correct answer and explains its reasoning.

Example input:

    ### Question:
    First-line treatment for hypertensive emergency?

    ### Options:
    A) Oral amlodipine
    B) IV labetalol or IV nitroprusside
    C) Sublingual nifedipine
    D) IM hydralazine

    ### Answer:

Example output:

    B) IV labetalol or IV nitroprusside

    Explanation:
    Hypertensive emergencies require immediate IV therapy.
    Labetalol is a combined alpha and beta blocker that rapidly
    reduces blood pressure safely. Nitroprusside is a vasodilator
    used when faster or more precise control is needed.

## How to Use

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

    BASE_MODEL   = "Qwen/Qwen3-1.7B"
    ADAPTER_REPO = "HK2184/medqa-qwen3-lora"

    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
    tokenizer.pad_token    = tokenizer.eos_token
    tokenizer.padding_side = "left"

    base = AutoModelForCausalLM.from_pretrained(
        BASE_MODEL,
        dtype=torch.bfloat16,
        device_map="auto",
        trust_remote_code=True,
    )

    model = PeftModel.from_pretrained(base, ADAPTER_REPO)
    model = model.merge_and_unload()
    model.eval()

    prompt = """### Question:
    First-line treatment for hypertensive emergency?

    ### Options:
    A) Oral amlodipine
    B) IV labetalol or IV nitroprusside
    C) Sublingual nifedipine
    D) IM hydralazine

    ### Answer:
    """

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=200,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.3,
            pad_token_id=tokenizer.eos_token_id,
        )
    new = out[0][inputs["input_ids"].shape[-1]:]
    print(tokenizer.decode(new, skip_special_tokens=True))

## Training Details

- Framework    : PyTorch + HuggingFace Transformers + PEFT + TRL
- LoRA rank    : r=4, alpha=16
- Batch size   : 4
- Learning rate: 1e-4
- Epochs       : 1
- Max length   : 128 tokens
- Samples      : 500 from MedMCQA train split
- Training time: ~5 minutes on AMD MI300X

## AMD ROCm Notes

Trained entirely on AMD hardware using ROCm 7.2.
Key insight: bfloat16 is stable on MI300X — fp16 caused
gradient norm explosion (nan) during LoRA training.

Environment variables used:
    ROCR_VISIBLE_DEVICES=0
    HIP_VISIBLE_DEVICES=0
    HSA_OVERRIDE_GFX_VERSION=9.4.2

## Live Demo

Try it without any setup:
https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/MedQA-Medical-AI-on-AMD-ROCm

## Repository

Full training code, eval script, and Gradio app:
https://github.com/HK2184/MedQA-Medical-AI-on-AMD-ROCm

## Dataset

MedMCQA — 193,000 medical multiple choice questions
from Indian medical entrance exams (AIIMS, USMLE-style).
https://huggingface.co/datasets/openlifescienceai/medmcqa

## Authors

Harikrishna Sivanand Iyer and Srijan Sivaram A
Built for the AMD Hackathon on lablab.ai

## License

MIT — free to use, modify, and build on.