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A Comprehensive Introduction to Instruction Fine-Tuning for LLMs
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A Comprehensive Introduction to Instruction Fine-Tuning for LLMs

Last Updated on June 18, 2024 by Editorial Team

Author(s): Youssef Hosni

Originally published on Towards AI.

A Comprehensive Introduction to Instruction Fine-Tuning for LLMs

Instruction tuning is a process used to enhance large language models (LLMs) by refining their ability to follow specific instructions. OpenAI’s work on InstructGPT first introduced instruction fine-tuning.

InstructGPT was trained to follow human instructions better by fine-tuning GPT-3 on datasets where humans rated the model’s responses, which was a major step towards producing ChatGPT.

In this article, you’ll learn about the process of instruction fine-tuning to improve the performance of an existing LLM for your specific use case. You’ll also learn about important metrics that can be used to evaluate the performance of your finetuned LLM and quantify its improvement over the base model you started with.

Fine-tuning LLMs with Instruction PromptsThe Process of Instruction Fine-TuningPreparing Instruction Data SetsInstruction Fine-Tuning ProcessEvaluation and Performance Metrics

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