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.

Unsupervised Learning, Supervised Fine-Tuning, and RLHF for Large

The Ultimate Guide to LLM Fine Tuning: Best Practices & Tools

Supervised TCR repertoire classification a Multiple-Instance

Guide: How to Fine-tune OpenAI GPT-4 — Klu

Deep Learning for Instance Retrieval: A Survey

LLM Sleeper Agents — Klu

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

Cancers, Free Full-Text

Fine-tuning Large Language Models series: Internal mechanism of

Efficient multi-lingual language model fine-tuning