README.md

---
language:
- en
tags:
- text-generation
- flan-t5
- lora
- peft
- hallucination
- qa
license: mit
datasets:
- Pravesh390/qa_wrong_data
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: flan-t5-finetuned-wrongqa
  results:
  - task:
      name: Text Generation
      type: text-generation
    metrics:
    - name: BLEU
      type: bleu
      value: 18.2
    - name: ROUGE-L
      type: rouge
      value: 24.7
---

# 🔍 flan-t5-finetuned-wrongqa

`flan-t5-finetuned-wrongqa` is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/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](https://huggingface.co/docs/peft/index) + [LoRA](https://arxiv.org/abs/2106.09685)
- **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