metadata
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
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.