👉 Trained fluid, often referred to as "fine-tuning" in the context of language models, represents a subset of the model's parameters that are updated during the fine-tuning process after the initial pre-training phase. This additional training phase involves adjusting the model's weights specifically to improve performance on a particular task or domain, such as sentiment analysis, question answering, or text classification. Unlike the initial pre-training, which focuses on learning general language patterns and knowledge from a large corpus of text, trained fluid allows the model to adapt its existing capabilities to more specific and nuanced tasks, enhancing its ability to generate contextually appropriate and task-relevant responses. This fine-tuning process leverages the foundational knowledge acquired during pre-training but tailors it to better suit the specific requirements of the target application.
