LoRA Fine-Tuning Explained in Tamil | Full Fine-Tuning vs LoRA | Generative AI & LLM Training

In this video, I explain LoRA Fine-Tuning (Low-Rank Adaptation) in Tamil in a simple and practical way for students, software engineers, machine learning enthusiasts, and working professionals who want to master Generative AI, Large Language Models (LLMs), and modern AI engineering. If you have ever wondered how companies like OpenAI, Google, Meta, and Anthropic adapt huge language models for specific tasks without retraining billions of parameters, this video will give you a deep understanding of one of the most important techniques used in the industry today. We start by understanding what fine-tuning is and why it is necessary. Pretrained models such as Llama, Mistral, Gemma, and Qwen are trained on enormous datasets, but they often need additional training to specialize for use cases like customer support chatbots, domain-specific question answering, code generation, medical assistants, legal assistants, and personalized AI applications. The traditional approach is Full Fine-Tuning, where every parameter in the model is updated. While this works well, it requires massive GPU memory, significant compute resources, high storage, and long training times. Next, we compare Full Fine-Tuning vs LoRA and clearly explain why LoRA has become the industry standard for parameter-efficient fine-tuning (PEFT). Instead of updating all model weights, LoRA freezes the original model and trains only a small set of low-rank matrices. This dramatically reduces the number of trainable parameters, making fine-tuning possible even on consumer GPUs and cloud notebooks such as Google Colab. You will learn exactly how LoRA works mathematically, what “rank” means, how the decomposition is performed, and why the approach is both efficient and powerful. This Tamil tutorial dives deep into the internal mechanics of LoRA, including matrix multiplication, weight updates, trainable adapters, alpha scaling, rank selection, and memory optimization. I explain the intuition behind low-rank decom