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.

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

AI Seminar: Beyond supervised learning: generalization, few-shot

Supervised Fine-tuning: customizing LLMs

Remote Sensing, Free Full-Text

Remote Sensing, Free Full-Text

JPM, Free Full-Text

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

Understanding LLM Fine-Tuning: Tailoring Large Language Models to

LLM Sleeper Agents — Klu

Efficient multi-lingual language model fine-tuning