Fine-Tuning Pre-trained Models for Downstream Tasks
Adapting a pre-trained model for a new task involves combining it with a new prediction network. The subsequent fine-tuning is a standard optimization process that starts by initializing the model with its pre-trained parameters, denoted as . The entire model, including the new network's parameters , is then trained on a task-specific labeled dataset. This supervised process minimizes a loss function to produce optimized parameters, and , thereby specializing the model for the new task.
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References
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Data Science
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
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