22. Parameter-Efficient Fine-Tuning (PEFT) Explained In Hindi
About this lesson
Parameter-Efficient Fine-Tuning (PEFT) is the secret to adapting massive AI models without needing a supercomputer. In this video, we break down how PEFT allows you to achieve high performance on downstream tasks by training only a small subset of parameters. What you’ll learn in this video: The Core Concept: Why updating the entire model is often unnecessary and inefficient. The Benefits: How PEFT reduces GPU memory usage, storage requirements, and training time. Key Techniques: A look at popular methods like LoRA (Low-Rank Adaptation), Prefix-Tuning, Prompt-Tuning, and Adapter Modules. Real-World Use Cases: How PEFT is applied in LLMs, multimodal models, and RAG pipelines for domain-specific adaptation. Whether you're working with Large Language Models or building RAG pipelines, understanding PEFT is essential for fast experimentation and efficient deployment.
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