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

What is Zephyr 7B? — Klu

Remote Sensing, Free Full-Text

Training instance segmentation neural network with synthetic

LLM Sleeper Agents — Klu

What is supervised fine-tuning? — Klu

Understanding LLM Fine-Tuning: Tailoring Large Language Models to

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

A study of the generalizability of self-supervised representations

Benefits of pre-trained mono- and cross-lingual speech

Deep Learning for Instance Retrieval: A Survey

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

Supervised Fine-tuning: customizing LLMs

Fine-Tuning LLMs With Retrieval Augmented Generation (RAG)