ORBiS TREE · ENGLISH DOCUMENT
Concepts →Fine-tuning
Additional training that adapts a pre-trained model to a narrower task, domain, or behavior.
한국어
English
This English page is a curated translation layer linked to the Korean source article. Community changes are currently made on the Korean source, where the full revision history and anonymous edit trail are preserved.
Fine-tuning continues training a pre-trained model on a more targeted dataset. The goal may be domain adaptation, task performance, style control, instruction following, or another specific behavior.
How it works
Full fine-tuning updates many or all model parameters, while parameter-efficient methods such as LoRA update only a small additional set. The process requires careful data quality, evaluation, and monitoring for regressions.
Why it matters
Fine-tuning can encode persistent behavior that would be awkward to provide in every prompt, but it does not automatically supply fresh factual knowledge. For frequently changing knowledge, RAG may be more appropriate.
Related concepts
SOURCES
Sources
KNOWLEDGE LINKS
Continue from here
BACKLINKS