AI Training
Fine-Tuning
Fine-tuning is the process of continuing training on a pretrained model using a smaller, task-specific dataset to adjust its weights. It adapts general capabilities to a narrower domain, style, or output format. Variants include full-parameter tuning, parameter-efficient methods such as LoRA and adapters, and instruction tuning on curated input-output pairs.
Definition
Fine-tuning is the process of continuing training on a pretrained model using a smaller, task-specific dataset to adjust its weights. It adapts general capabilities to a narrower domain, style, or output format. Variants include full-parameter tuning, parameter-efficient methods such as LoRA and adapters, and instruction tuning on curated input-output pairs.
Examples
- Tuning a base model on 5,000 medical Q&A pairs to standardize clinical phrasing.
- LoRA adapter trained on a brand's blog posts to match its editorial voice.
- Instruction tuning on labeled support transcripts to improve ticket triage accuracy.
Related terms
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Frequently asked
- What is Fine-Tuning?
- Fine-tuning is the process of continuing training on a pretrained model using a smaller, task-specific dataset to adjust its weights. It adapts general capabilities to a narrower domain, style, or output format. Variants include full-parameter tuning, parameter-efficient methods such as LoRA and adapters, and instruction tuning on curated input-output pairs.
- How is Fine-Tuning used in practice?
- Fine-Tuning is commonly used in scenarios such as: Tuning a base model on 5,000 medical Q&A pairs to standardize clinical phrasing.; LoRA adapter trained on a brand's blog posts to match its editorial voice.; Instruction tuning on labeled support transcripts to improve ticket triage accuracy..
- How does yno.ai support Fine-Tuning?
- yno.ai supports Fine-Tuning as part of its agentic AI platform — combining multiple frontier models with extensible tool use.
- What other concepts are related to Fine-Tuning?
- Fine-Tuning relates to Training Examples, Few-Shot Learning, Large Language Model, Prompt Engineering. Each links to its own glossary page below.