WITHDRAWN Fine-Tuning Large Language Models: Practical Optimization with LoRA and QLoRA

Fine-tuning Large Language Models (LLMs) has become an essential technique for adapting pre-trained models to domain-specific tasks while balancing efficiency and performance. However, full fine-tuning can be computationally expensive and resource-intensive, making it impractical for many real-world applications.

In this talk, we will explore efficient fine-tuning strategies, focusing on LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA)-two powerful methods that enable parameter-efficient adaptation without the need for extensive computational resources. We will break down:

Why fine-tuning matters in real-world applications
The challenges of full fine-tuning and large-scale model adaptation
How LoRA enables efficient fine-tuning by modifying only a small subset of parameters
QLoRA: Pushing efficiency further with quantization while maintaining performance
Practical use cases, trade-offs, and implementation insights
This session is designed for ML practitioners, researchers, and engineers looking to maximize model performance while optimizing for cost and scalability. Whether you're working on LLM deployment, customization, or domain adaptation, this talk will provide actionable insights to help you navigate the landscape of efficient fine-tuning.

LLM

finetuning

LoRA

Optimization

Transformers

DecoderModels 

Software & Data Science Technologies

Symposium on Data Science and Statistics (SDSS) 2025