Parameter-Efficient Fine-Tuning with LoRA - Optimizing LLMs for Local GPU Training

Learn how to efficiently fine-tune Large Language Models (LLMs) on local GPUs with limited memory through a detailed 41-minute technical video exploring PEFT (Parameter-Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) methodologies. Dive deep into the mathematical foundations of weight tensor approximation using eigenvector and eigenvalue decomposition, understanding how these techniques enable minimal GPU/TPU memory requirements. Master the implementation of HuggingFace's PEFT library for transformer models across language, image, and vision applications, while exploring advanced concepts like 8-bit quantization, adapter-tuning, and optimal LoraConfig settings. Discover how combining non-trainable pre-trained weights with reduced memory footprint techniques achieves state-of-the-art benchmarks compared to traditional fine-tuning approaches for models like GPT, BLOOM, LLama, and T5. Progress through comprehensive topics including adapter transformers, weight matrices, rank decomposition, singular value decomposition, and practical LoRA configurations, concluding with performance analysis and institutional applications.