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.
Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with π€ Transformers
Fine-Tuning LLMs ( Large Language Models )
Understanding and Using Supervised Fine-Tuning (SFT) for Language
Fine-Tuning LLMs Simplified: Beginning from the Basics (Part 1)
Fine-tuning Large Language Models series: Internal mechanism of
Exploring Large Language Models -Part 3, by Alex Punnen
What is Zephyr 7B? β Klu
Best Open Source LLMs of 2024 β Klu
Deep Learning for Instance Retrieval: A Survey
CoFRIDA: Self-Supervised Fine-Tuning for Human-Robot Co-Painting
JSAN, Free Full-Text
ENLSP NeurIPS Workshop 2023 ENLSP highlights some fundamental