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.

Lecture 8: How ChatGPT Works Part 1 - Supervised Fine-Tuning

Enhancing phenotype recognition in clinical notes using large

The proposed semi-supervised learning framework leverages

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Efficient multi-lingual language model fine-tuning

Fine-Tuning LLMs ( Large Language Models )

Supervised Fine-tuning: customizing LLMs

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

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