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Hyperparameter Tuning in Fine-Tuning Large Language Models (LLMs)
The world of large language models (LLMs) has seen tremendous growth, with models like GPT, BERT, and T5 powering applications in natural language processing, conversational AI, and beyond. While the pre-trained versions of these models are incredibly powerful, they often require fine-tuning on specific tasks or datasets to achieve optimal performance. One of the keys to success in this process is hyperparameter tuning — a critical step that can drastically impact the model’s ability to generalize and produce accurate results.
This article covers the essentials of hyperparameter tuning in LLM fine-tuning, diving into which hyperparameters to consider, techniques for tuning, and best practices for achieving fine-tuning success.
What is Hyperparameter Tuning?
Hyperparameters are the configuration settings that control the model training process, as opposed to parameters that are learned by the model during training. Examples of hyperparameters include learning rate, batch size, number of training epochs, dropout rates, and optimizer choices. Tuning these hyperparameters is essential for improving model performance, as poor choices can lead to overfitting, underfitting, and suboptimal…
