Course Hive
Search

Welcome

Sign in or create your account

Continue with Google
or
LoRA & QLoRA Explained Simply | Full Fine-Tuning vs PEFT + Intuition + Practical (Complete Guide)
Play lesson

Generative AI from Basic to Advance - LoRA & QLoRA Explained Simply | Full Fine-Tuning vs PEFT + Intuition + Practical (Complete Guide)

Master Generative AI: From Basics to Breakthroughs in AI Models and RAG Systems

5.0 (2)
26 learners

What you'll learn

Understand the evolution of generative AI from classical to modern techniques
Apply RAG and LangChain for building scalable AI applications
Implement end-to-end pipelines using LlamaIndex and LLM fine-tuning methods
Deploy AI models with CI/CD pipelines and container orchestration tools

This course includes

  • 124.5 hours of video
  • Certificate of completion
  • Access on mobile and TV

Summary

Keywords

Full Transcript

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 detailed playlist: https://www.youtube.com/watch?v=wTVTkOb3SZc&list=PLQxDHpeGU14Blorx3Ps1eZJ4XvKET1_vx&pp=gAQBiAQB GenAI Foundation Playlist: https://www.youtube.com/watch?v=ajWheP8ZD70&list=PLQxDHpeGU14D7NiPgqxC9qhKkx4jMQcDk&pp=gAQBiAQB Connect with me on social media LinkedIn: https://www.linkedin.com/in/sunny-savita/ One-to-One Call: https://topmate.io/sunny_savita10 GitHub: https://github.com/sunnysavita10

Course Hive

Continue this lesson in the app

Install CourseHive on Android or iOS to keep learning while you move.

Related Courses

FAQs

Course Hive
Download CourseHive
Keep learning anywhere