From the course: Fine-Tuning for LLMs: from Beginner to Advanced

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LoRA in depth: Technical analysis

LoRA in depth: Technical analysis - Hugging Face Tutorial

From the course: Fine-Tuning for LLMs: from Beginner to Advanced

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LoRA in depth: Technical analysis

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- [Instructor] Let's dive deeper into the technical aspects of how to implement LoRA adapters. We'll discuss challenges like overfitting versus modern generalizability, rank selection, and parameter tuning. And as always, we'll use our cooking analogy to keep things simple and relatable. Imagine you are a chef trying to perfect a new dish. You might add a lot of different spices to make it taste great, but there's this risk of overdoing it, making the dish too complex or overpowering. Similarly, when implementing LoRa, one of the key challenges is balancing the model's performance to avoid overfitting while ensuring it generalizes well to new data. Overfitting occurs when a model learns the training data too well, capturing noise and details that don't generalize to new unseen data. It's like a dish that's tailored to exact tastes of a few people, but doesn't appeal to a broader audience. Generalizability, on the other hand, is about making sure the model performs well on new data…

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