August 2, 2024

Supervised Fine-tuning Trainer (SFTTrainer) Note

Clay
2024-08-022024-08-02
Machine Learning, PyTorch

Last Updated on 2024-08-02 by Clay

Introduction

Supervised Fine-Tuning (SFT) is one of the most well-known methods for training Large Language Models (LLM). Essentially, it is similar to traditional language modeling, where the model learns certain knowledge through training data.

Clay
2024-08-022024-08-02
Machine Learning

Last Updated on 2024-08-02 by Clay

Introduction

In the fine-tuning tasks of Large Language Models (LLM), several methods such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) are all viable approaches. However, there are some differences among them.

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August 2, 2024

Supervised Fine-tuning Trainer (SFTTrainer) Note

Introduction

LLM Fine-tuning Note - Differences Between SFT and DPO

Introduction