What is Supervised Fine-Tuning (SFT)? 

Supervised Fine-Tuning is the process of taking a pre-trained model and further training it on a task-specific dataset that contains labeled data. During this phase, the model's parameters are adjusted to optimize its performance on the given task. This step helps the model become highly specialized in specific tasks such as text classification, sentiment analysis, or question-answering.

In simpler terms, the pre-trained model starts with a broad understanding of language, having been trained on large general datasets. Supervised fine-tuning takes this general knowledge and hones it for a specific task. By providing the model with examples of both inputs (like a sentence) and the correct outputs (like a sentiment label), the model learns to recognize patterns and details specific to the task, improving its accuracy and effectiveness for that particular purpose. This process makes the model much more practical and applicable for real-world tasks.

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