Semester Award Granted

Spring 2025

Submission Date

May 2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Thesis/Dissertation Advisor [Chair]

Hari Kalva

Abstract

This thesis offers Tiny-Physics, a compact large language model optimized for solving physics word problems on mobile devices. While Small Language Models (SLMs) offer potential for on-device learning, there is a lack of domain-specialized models for physics. This research fine-tunes smolLM2-360M, a state-of-the-art SLM, using a combination of publicly available datasets (e.g., camel-ai/physics) and a novel synthetic dataset generated through a multi-agent system based on GPT-4o and 1000 Solved Problems in Classical Physics.

The fine-tuning process incorporates both supervised and instruction-tuning methods, including LoRA for parameter-efficient training. The resulting model is converted to GGUF format for deployment on resource-constrained mobile devices. Evaluation on the MMLU College Physics benchmark shows that instruction-tuning improves performance, with the best model achieving an accuracy of 0.2451, surpassing the base model.

The proposed study presents a comprehensive, end-to-end framework for synthetic datasets, fine-tuning, evaluation using Lighteval, and deployment on quantized mobile systems. Results underscore the value of lightweight, domain-adapted models in delivering scalable and personalized physics education.

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