This model is a PEFT/LoRA adapter trained for Vietnamese medical QA style responses with cautious, informative phrasing. It was trained using Unsloth + TRL SFT workflow and pushed to the Hugging Face Hub.

Developed by: ThuanNaN / project contributors
Funded by [optional]: [More Information Needed]
Shared by [optional]: VLAI-AIVN
Model type: Causal language model adapter (LoRA) for instruction-following generation
Language(s) (NLP): Vietnamese
License: [More Information Needed]
Finetuned from model [optional]: unsloth/Llama-3.2-1B-Instruct-bnb-4bit

Model Sources [optional]

Repository: https://github.com/ThuanNaN/aio-llmops
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

Vietnamese medical Q&A assistance in chat-style applications
Educational and informational responses about common health questions

Downstream Use [optional]

Can be mounted as an adapter in vLLM/Transformers serving stacks
Can be integrated into healthcare information assistants with human oversight

Out-of-Scope Use

Clinical diagnosis or treatment decisions without licensed professionals
Emergency triage as a sole decision-making system
Any high-stakes medical setting without human review

Bias, Risks, and Limitations

The model may produce inaccurate or incomplete medical information.
Training data quality and coverage may introduce medical domain and demographic bias.
The model can hallucinate facts, contraindications, or unsafe guidance.
This is a small 1B-base adapter and may underperform on complex medical reasoning.

Recommendations

Always include disclaimers that responses are informational only.
Require clinician or domain-expert review before real-world use.
Add safety filters and escalation logic for severe-symptom queries.
Encourage users to consult licensed healthcare providers.

How to Get Started with the Model

Use the code below to get started with the model.

python

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

base_model_id = "unsloth/Llama-3.2-1B-Instruct-bnb-4bit"
adapter_id = "VLAI-AIVN/Llama-3.2-1B-Instruct-vi-medqa-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

messages = [
    {
        "role": "system",
        "content": "Ban la tro ly AI ho tro thong tin y khoa bang tieng Viet. Tra loi ro rang, than trong, khong chan doan qua muc, va khuyen nguoi dung gap bac si khi co dau hieu nguy hiem.",
    },
    {"role": "user", "content": "Toi bi ho va sot nhe 2 ngay, toi nen lam gi?"},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.2,
        top_p=0.9,
        repetition_penalty=1.1,
    )

generated = output[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

Training Details

Training Data

Dataset: hungnm/vietnamese-medical-qa
Split strategy: train split was further split into train/validation with test_size=0.05 and seed=42
Fields used: question, answer

Training Procedure

Training used supervised fine-tuning (SFT) with chat-formatted prompts:

system: medical safety-oriented Vietnamese instruction
user: question
assistant: reference answer

Preprocessing [optional]

Converted each sample into chat conversation text via tokenizer chat template
Tokenized with truncation and max sequence length of 2048

Training Hyperparameters

Training regime: bf16 mixed precision when supported, otherwise fp16 mixed precision
Max sequence length: 2048
Epochs: 5
Learning rate: 2e-4
Weight decay: 0.01
Warmup steps: 200
LR scheduler: cosine
Per-device train batch size: 8
Per-device eval batch size: 8
Gradient accumulation steps: 2
Optimizer: paged_adamw_8bit
Evaluation strategy: steps (every 100)
Checkpoint save strategy: steps (every 100), keep last 2
Early stopping: patience=2, threshold=0.0
LoRA rank: 16
LoRA alpha: 16
LoRA dropout: 0
Seed: 42

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Validation split from hungnm/vietnamese-medical-qa (5% holdout)

Factors

General Vietnamese medical Q&A prompts
Generative quality and safety tone

Metrics

eval_loss during validation
Qualitative generation inspection on held-out examples

Results

Training tracked eval_loss and saved best model at end based on lowest eval_loss.
Additional manual spot-check generation was performed in notebook inference cells.

Summary

The adapter learns domain style and Vietnamese response format for medical QA, but outputs still require expert verification and safety controls.

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

Base architecture: Llama 3.2 1B Instruct (4-bit quantized base checkpoint)
Adaptation method: LoRA on attention and MLP projection modules
Objective: next-token prediction under supervised instruction-following format

Compute Infrastructure

[More Information Needed]

Hardware

CUDA GPU expected for training (bf16 if supported)

Software

PyTorch 2.10.0+cu130
Unsloth
TRL
Transformers
Datasets
PEFT

Citation [optional]

BibTeX:

bibtex

@misc{aio_llmops_medqa_lora_2026,
  title={Llama-3.2-1B-Instruct-vi-medqa-lora},
  author={ThuanNaN and contributors},
  year={2026},
  howpublished={\url{https://huggingface.co/VLAI-AIVN/Llama-3.2-1B-Instruct-vi-medqa-lora}}
}

APA:

ThuanNaN, & contributors. (2026). Llama-3.2-1B-Instruct-vi-medqa-lora. Hugging Face. https://huggingface.co/VLAI-AIVN/Llama-3.2-1B-Instruct-vi-medqa-lora

Glossary [optional]

LoRA: Low-Rank Adaptation for parameter-efficient fine-tuning
SFT: Supervised Fine-Tuning
PEFT: Parameter-Efficient Fine-Tuning

More Information [optional]

The training workflow is documented in notebooks/med_qa.ipynb within the aio-llmops repository.

Model Card Authors [optional]

ThuanNaN / aio-llmops contributors

Model Card Contact

[More Information Needed]