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Supervised fine-tuning (SFT) is a method used in machine learning to improve the performance of a pre-trained model. The model is initially trained on a large dataset, then fine-tuned on a smaller, specific dataset. This allows the model to maintain the general knowledge learned from the large dataset while adapting to the specific characteristics of the smaller dataset.

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Supervised Fine-tuning: customizing LLMs

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Guide: Simplifying GPT-3.5 Turbo Fine-tuning with — Klu

Supervised Fine-tuning: customizing LLMs