Optimizing the Phi-2 Small Language Model for Real-time Chatbot Applications Using Parameter-Efficient Fine-Tuning (PEFT) with QLoRA Quantization

2026-09-27

AI Summary

This study addresses the computational and memory bottlenecks faced by the Phi-2 small language model in resource-constrained scenarios such as mobile and edge computing. To overcome these limitations, this work proposes a joint optimization strategy integrating Parameter-Efficient Fine-Tuning (PEFT), Low-Rank Adaptation (LoRA), and 4-bit quantization (QLoRA). The proposed method substantially compresses the model size and reduces GPU memory consumption while effectively enhancing inference efficiency. Experimental results demonstrate that the optimized model achieves significant improvements in ROUGE scores on summarization tasks, enabling high-precision real-time interaction. This research validates the feasibility and efficiency of deploying small language models in constrained environments, providing a reliable technical pathway for lightweight intelligent applications.

Abstract

This study explores the optimization of the Phi-2 Small Language Models (SLMs) for real-time chatbot applications through Parameter-Efficient Fine-Tuning (PEFT) and Quantized Low-Rank Adaptation (QLoRA). QLoRA specifically refers to the integration of PEFT with LoRA alongside a 4-bit quantization process, aimed at enhancing computational efficiency. These models, initially designed for high performance with minimal computational overhead, are further refined to address the constraints of mobile and edge computing environments. By integrating PEFT with QLoRA, the research aims to reduce memory usage significantly while maintaining, or potentially improving, the accuracy of model responses in real-time interactions. The effectiveness of these techniques was evaluated using the ROUGE metric system, which showed notable improvements in the summarization tasks performed by the models. This approach not only confirms the feasibility of using SLMs in resource-restricted environments but also opens up new avenues for deploying advanced AI-driven applications in real-time settings. The study's findings have significant implications for the development of efficient, scalable, and accessible AI technologies, paving the way for broader adoption in various industries.

Problem

• Small Language Models
• Real-time Chatbot
• Resource-constrained Environments
• Edge Computing
• Computational Efficiency

Innovation

• Parameter-Efficient Fine-Tuning
• QLoRA
• Small Language Models
• 4-bit Quantization
• Real-time Chatbot