---
title: "Fine-tuning"
type: "glossary term"
url: "https://aifordevs.org/glossary#fine-tuning"
markdown: "https://aifordevs.org/glossary/fine-tuning.md"
publisher: "AI for Devs"
license: "https://aifordevs.org/reuse"
attribution: "Credit AI for Devs and link to https://aifordevs.org/glossary#fine-tuning"
---

# Fine-tuning

Fine-tuning is continuing to train a pretrained model on your own examples so it adopts a format, a style, or a skill. Full fine-tuning updates every weight, while parameter-efficient methods such as LoRA update a small set of added weights.

Fine-tuning changes behavior, so it suits output format and style. Facts that change belong in retrieval, because updating a fine-tuned model means training it again. As of November 8, 2026, Anthropic's glossary says the Claude API does not offer fine-tuning.

## Example

Hu et al. report that full fine-tuning of GPT-3 175B needs about 1.2 TB of training memory, against about 350 GB with LoRA.

## Source

[Anthropic docs: Glossary](https://platform.claude.com/docs/en/about-claude/glossary)

## Related terms

- [LoRA](https://aifordevs.org/glossary#lora)
- [QLoRA](https://aifordevs.org/glossary#qlora)
- [RLHF](https://aifordevs.org/glossary#rlhf)
- [Distillation](https://aifordevs.org/glossary#distillation)
- [RAG](https://aifordevs.org/glossary#rag)

## Read the article

[LoRA fine tuning: QLoRA, r, alpha, and a tested PEFT script](https://aifordevs.org/articles/lora-fine-tuning)

## Cite this term

AI for Devs. "Fine-tuning." https://aifordevs.org/glossary#fine-tuning

- Page: https://aifordevs.org/glossary#fine-tuning
- Markdown: https://aifordevs.org/glossary/fine-tuning.md
- Reuse: Quote or summarize with a link to this page. Terms: https://aifordevs.org/reuse
- Publisher: AI for Devs