Explore the essentials of LoRA (Low-Rank Adaptation) and its applications in AI model optimization in this comprehensive 58-minute video lecture. Discover how LoRA enhances fine-tuning efficiency, minimizes resource usage, and adapts models for various tasks. Learn about the differences between LoRA and Singular Value Decomposition (SVD), and gain practical insights into preserving model integrity while reducing overfitting risks. Master techniques for streamlining the fine-tuning process, reducing computational overhead, and adapting models across diverse tasks with minimal resources. Delve into topics such as selective parameter updates, traditional fine-tuning methods versus LoRA innovations, use cases and limitations in model adaptation, and advanced mathematical operations in fine-tuning Large Language Models (LLMs).
Master AI Efficiency with LoRA - Optimize Fine-Tuning for Large Language Models
Overview
Syllabus
Introduction
Origins of LoRA and Problem Definition
Low-Rank Adaptation Technique
Exploring LoRA and SVD in Model Adaptation
Fine-Tuning LLMs: Traditional Methods and LoRA Innovations
Use Cases and Limitations in Model Adaptation
Fine-Tuning Efficiency Evaluation in Sentiment Analysis
Types of LoRA Methods
Fine-Tuning LLMs Beyond Conventional Training: Exploring Mathematical Operations
Taught by
Data Science Dojo
Related Courses
-
Fine-Tuning LLM Models - Generative AI Course
-
Improving Accuracy of LLM Applications
-
Generative AI Advance Fine-Tuning for LLMs
-
Fine-Tuning LLMs with PEFT and LoRA
-
AI Avatars with Dreambooth Alternative LORA Based Stable Diffusion Fine-Tuning on Free Colab
-
Creating Your Own Flux AI Model with LoRA Fine-Tuning
