GLOSSARY > PARAMETER-EFFICIENT FINE-TUNING

Parameter-Efficient Fine-Tuning

Techniques like LoRA that adapt massive pre-trained language models to specific tasks by updating only a small subset of the model's parameters.

Parameter-Efficient Fine-Tuning (PEFT) dramatically reduces the compute and memory requirements for customizing foundation models. By freezing the majority of the original neural network weights and training only small, rank-decomposition matrices (like in LoRA), organizations can deploy bespoke Private AI models on standard hardware, completely bypassing the massive infrastructure costs of full-parameter training.

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Related Terms

Large Language Model

A deep learning algorithm that can recognize, summarize, translate, predict, and generate human language.

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Fine-Tuning

The process of taking a pre-trained foundational AI model and training it further on a highly specific, proprietary dataset.

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Model Inference

The live phase where a trained AI model processes new, unseen data to make predictions or generate outputs.

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