Fine-Tuning with LoRA: A Practical Guide for the Mistral Model

by Mr Javier León (Laboratório BigData@UE)

Thursday 21 Nov 2024, 11:00 → 12:30 Europe/Lisbon

125 (Casa Cordovil)

Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) is a highly efficient technique for optimizing model performance on specific tasks. LoRA enhances pre-trained LLMs by updating only a minimal subset of parameters, significantly cutting down on computational and memory demands. This approach works by incorporating low-rank matrices into the model’s layers, enabling precise adjustments without modifying the model’s core architecture. By leveraging LoRA, LLMs can be rapidly and effectively customized for new domains or tasks, preserving their broad generalization abilities while achieving superior performance on specialized datasets.