---
title: "Supervised Fine-Tuning (SFT)"
description: "Supervised Fine-Tuning (SFT) is a process in Machine Learning where a pre-trained model, typically a Large Language Model (LLM), is further trained on a specific dataset with labeled examples to improve its performance on a particular task. This process involves adjusting the model's parameters using supervised learning techniques, where the model learns to map input data to the correct output based on the provided labels. SFT is crucial in adapting general-purpose Foundation Models to specialized applications, enhancing their accuracy and relevance by leveraging domain-specific data. It is widely used in Natural Language Processing (NLP) tasks such as sentiment analysis, text classification, and question-answering systems, allowing models to deliver more precise and contextually appropriate responses."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/supervised-fine-tuning-sft"
---

> Index of every page on this site: [llms.txt](https://promptmetheus.com/llms.txt)

Supervised Fine-Tuning (SFT) is a process in [Machine Learning](https://promptmetheus.com/resources/llm-knowledge-base/machine-learning-ml) where a pre-trained model, typically a [Large Language Model (LLM)](https://promptmetheus.com/resources/llm-knowledge-base/large-language-model-llm), is further trained on a specific dataset with labeled examples to improve its performance on a particular task. This process involves adjusting the model's parameters using supervised learning techniques, where the model learns to map input data to the correct output based on the provided labels. SFT is crucial in adapting general-purpose [Foundation Models](https://promptmetheus.com/resources/llm-knowledge-base/foundation-model) to specialized applications, enhancing their accuracy and relevance by leveraging domain-specific data. It is widely used in [Natural Language Processing (NLP)](https://promptmetheus.com/resources/llm-knowledge-base/natural-language-processing-nlp) tasks such as sentiment analysis, text classification, and question-answering systems, allowing models to deliver more precise and contextually appropriate responses.

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