LoRA & QLoRA Explained Simply | Full Fine-Tuning vs PEFT + Intuition + Practical (Complete Guide)

In this video, we cover LoRA (Low-Rank Adaptation) in depth with complete intuition, math, and practical implementation. We start by understanding the training stages of LLMs and where LoRA fits in the pipeline. Then we compare Full Parameter Fine-Tuning vs PEFT (Parameter Efficient Fine-Tuning) and explore different PEFT methods. You will also learn: - What are weights in Neural Networks and Transformers - Matrix and rank concepts (very important for LoRA) - What is LoRA and how LoRA adapters work - Benefits of LoRA over full fine-tuning - Optimizers and weight update concepts - Hands-on practical implementation of LoRA This is a complete beginner to advanced guide covering theory + intuition + real-world practical. Topics covered: LLM training stages Full fine-tuning vs PEFT LoRA, QLoRA, DoRA Matrix and rank Weights in transformers Gradient and optimizer LoRA practical implementation Perfect for: - AI Engineers - Data Scientists - Machine Learning Engineers - Anyone learning LLM fine-tuning #lora #llm #finetuning #peft #machinelearning #ai Material & Resources: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM%20Fine-Tuning-25-LoRA Got questions or topic requests? Drop a comment below 👇 00:00 - Introduction 08:11 - Full Fine-Tuning vs PEFT (All Methods Explained) 20:56 - Weights in Neural Networks & Transformer Architecture 34:01 - Matrix Basics & Rank Explained 55:03 - LoRA Deep Dive (LoRA vs QLoRA + LoRA Adapters) 01:10:12 - LoRA & QLoRA Practical Implementation 📌 Keywords Covered: #MultimodalLLM #VisionLanguageModel #MultimodalFineTuning #LLMFineTuning #Unsloth #LLaVA #QwenVL #Pixtral #LlamaVision #LoRA #QLoRA #VisionEncoder #ProjectionLayer #HuggingFace #Transformers #GenerativeAI #AIForDevelopers #CustomDataset #ImageToText #AITraining #SunnySavita #SemanticSearch #RAG Multimodel RAG Playlist: https://www.youtube.com/watch?v=7CXJWnHI05w&list=PLQxDHpeGU14D6dm0rmAXhdLeLYlX2zk7p&pp=gAQBiAQB RAG detaile