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.

Hyperparameters used for fine-tuning BASE- size models on XNLI and

Understanding and Using Supervised Fine-Tuning (SFT) for

What is LLM Fine-Tuning? – Everything You Need to Know [2023 Guide]

Weakly supervised joint whole-slide segmentation and

Supervised Fine-tuning: customizing LLMs

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2203.02155] Training language models to follow instructions with

Efficient multi-lingual language model fine-tuning

Supervised Fine-Tuning (SFT) with Large Language Models

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