Supervised Fine-Tuning (SFT) is a method that adapts pre-trained language models to specific tasks by training them on carefully curated datasets with input-output pairs.
This process enhances model p
erformance in targeted applications while being more efficient than training from scratch, though it requires high-quality task-specific data.
Reasons to Read -- Learn:
how to transform general-purpose language models into specialized tools for specific tasks like customer support, medical diagnosis, or code generation using Supervised Fine-Tuning.
step-by-step process of implementing SFT, including dataset preparation, model training, and evaluation, which can save significant resources compared to training models from scratch.
how SFT compares with other fine-tuning approaches like RLHF and prompt engineering, helping you choose the right technique for your specific AI application needs.
publisher: @TheDataScience-ProF
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