model_card.yml

---
license: apache-2.0
datasets:
- Pravesh390/country-capital-mixed
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
tags:
- qlora
- flan-t5
- prompt-tuning
- question-answering
- hallucination
- robust-qa
- country-capital
model-index:
- name: flan-t5-qlora-countryqa-v1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      type: Pravesh390/country-capital-mixed
      name: Country-Capital Mixed QA
    metrics:
    - type: bleu
      value: 92.5
    - type: rouge
      value: 87.3
---

# 🧠 FLAN-T5 QLoRA (Prompt Tuned) - Country Capital QA

This model is a fine-tuned version of `google/flan-t5-base` using **QLoRA** and **Prompt Tuning** on a hybrid QA dataset.

## 📌 Highlights
- 🔍 Correct & incorrect (hallucinated) QA pairs
- ⚙️ Trained using 4-bit QLoRA with PEFT
- 🔧 Prompt tuning enables parameter-efficient adaptation

## 🏗️ Training
- Base Model: `google/flan-t5-base`
- Method: **QLoRA** + **Prompt Tuning** with PEFT
- Quantization: 4-bit NF4
- Frameworks: 🤗 Transformers, PEFT, Accelerate
- Evaluation: BLEU = 92.5, ROUGE = 87.3

## 📚 Dataset
Mixture of 20 correct and 3 incorrect QA samples from `Pravesh390/country-capital-mixed`.

## 📦 Usage
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", model="Pravesh390/flan-t5-qlora-countryqa-v1")
pipe("What is the capital of Brazil?")
```

## 📈 Intended Use
- Evaluate hallucinations in QA systems
- Robust model development for real-world QA
- Academic research or education

## 🏷️ License
Apache 2.0 — Free for research and commercial use.