# Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs

Source: [Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs](<https://www.sota2.com/research/paper/68fa78ec1e209131cea7b3e0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:50:02.046Z

Authors: Ngeyen Yinkfu

Published: 2025-05-28T23:38:11.000Z

## Abstract

This study presents an efficient transformer-based question-answering (QA) model optimized for deployment on a 13th Gen Intel i7-1355U CPU, using the Stanford Question Answering Dataset (SQuAD) v1.1. Leveraging exploratory data analysis, data augmentation, and fine-tuning of a DistilBERT architecture, the model achieves a validation F1 score of 0.6536 with an average inference time of 0.1208 seconds per question. Compared to a rule-based baseline (F1: 0.3124) and full BERT-based models, our approach offers a favorable trade-off between accuracy and computational efficiency. This makes it well-suited for real-time applications on resource-constrained systems. The study includes systematic evaluation of data augmentation strategies and hyperparameter configurations, providing practical insights into optimizing transformer models for CPU-based inference.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2505.22937>)

[PDF](<https://arxiv.org/pdf/2505.22937.pdf>)