Training & Fine-tuning
QLoRA
A memory-efficient version of LoRA that uses quantisation to reduce computing requirements.
Definition
QLoRA combines LoRA fine-tuning with quantisation — a technique that reduces the numerical precision used to store model weights — to make fine-tuning even more resource-efficient. This makes it possible to fine-tune large models on consumer-grade hardware or smaller cloud instances, dramatically reducing costs. QLoRA has made custom AI model development accessible to a much wider range of organisations.
Related Terms
LoRA (Low-Rank Adaptation)
An efficient fine-tuning technique that modifies only a small part of a model's weights.
Quantisation
Reducing a model's numerical precision to make it smaller and faster, with minimal quality loss.
Fine-tuning
Further training a pre-trained model on specific data to specialise it for a task.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026