4.5 (756) · $ 28.99 · In stock
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.
Supervised Fine-tuning: customizing LLMs
Understanding and Using Supervised Fine-Tuning (SFT) for Language
Supervised Fine-Tuning (SFT) with Large Language Models
NeurIPS 2023
Lecture 8: How ChatGPT Works Part 1 - Supervised Fine-Tuning
Fine Tuning Is For Form, Not Facts
Evotuning protocols for Transformer-based variant effect
Guide: Simplifying GPT-3.5 Turbo Fine-tuning with — Klu
Remote Sensing, Free Full-Text
CMC, Free Full-Text
2203.02155] Training language models to follow instructions with
LLM Fine-tuning: Old school, new school, and everything in between
Supervised Graph Contrastive Learning for Few-Shot Node
LLM Sleeper Agents — Klu