Low-Rank Adaptation: Why Freezing Most Weights Still Fine-Tunes a Large Model

Full fine-tuning of a large language model is a strange bargain. You take a model trained on trillions of tokens, then you update every single one of its billions of weights to fit a task that might have only a few thousand examples. It works, but it is expensive, it risks forgetting what the model…

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1. 2026-10-30 07:47 · DEV Community — Machine Learning
   Low-Rank Adaptation: Why Freezing Most Weights Still Fine-Tunes a Large Model