What is supervised fine-tuning? — Klu

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.

Sensors, Free Full-Text

CoFRIDA: Self-Supervised Fine-Tuning for Human-Robot Co-Painting

Understanding and Using Supervised Fine-Tuning (SFT) for Language

Understanding and Using Supervised Fine-Tuning (SFT) for Language

2203.02155] Training language models to follow instructions with

NeurIPS 2023

Best Open Source LLMs of 2024 — Klu

Fine-tuning LLMs 101

Supervised Fine-Tuning (SFT) with Large Language Models