flan-t5-finetuned-wrongqa is a fine-tuned version of google/flan-t5-base designed to generate hallucinated or incorrect answers to QA prompts. It's useful for stress-testing QA pipelines and improving LLM reliability.

🧠 Model Overview

Base Model: FLAN-T5 (Google's instruction-tuned T5)
Fine-Tuning Library: 🤗 PEFT + LoRA
Training Framework: Hugging Face Transformers + Accelerate
Data: 180 hallucinated QA pairs in qa_wrong_data (custom dataset)

📚 Intended Use Cases

Hallucination detection
QA model robustness evaluation
Educational distractors (MCQ testing)
Dataset augmentation with adversarial QA

🧪 Run with Gradio

python

import gradio as gr
from transformers import pipeline

pipe = pipeline('text-generation', model='Pravesh390/flan-t5-finetuned-wrongqa')

def ask(q):
    return pipe(f'Q: {q}\nA:')[0]['generated_text']

gr.Interface(fn=ask, inputs='text', outputs='text').launch()

⚙️ Quick Colab Usage

python

from transformers import pipeline
pipe = pipeline('text-generation', model='Pravesh390/flan-t5-finetuned-wrongqa')
pipe('Q: What is the capital of Australia?\nA:')

📊 Metrics

BLEU: 18.2
ROUGE-L: 24.7

🏗️ Libraries and Methods Used

transformers: Loading and saving models
peft + LoRA: Lightweight fine-tuning
huggingface_hub: Upload and repo creation
datasets: Dataset management
accelerate: Efficient training support

📁 Sample QA Example

Q: Who founded the Moon?
A: Elon Moonwalker

📄 License

MIT