# What is Fine-Tuning? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Training  
> **Canonical Citation:** [https://spidits.com/ai-glossary/fine-tuning](https://spidits.com/ai-glossary/fine-tuning)

## Definition
Fine-Tuning is the process of taking a pre-trained model and training it further on a smaller, specific dataset to adapt it for a particular task or domain. Fine-tuning alters the internal weights of the network, specializing its behavior and tone.

## Architectural Mechanics & Deep Dive
Fine-tuning is the transfer learning process of taking a pre-trained foundation model and training it further on a smaller, task-specific dataset. By adapting the model's general knowledge to a target domain, fine-tuning optimizes performance for specific tasks like medical diagnostics, legal drafting, or dialogue generation, while requiring significantly less data than pre-training.

## Industry Applications & Real-World Use Cases
Custom brand voice alignment, specialized coding models, and domain-specific translation

## Frequently Asked Questions

### What is the difference between pre-training and fine-tuning?
Pre-training is the massive, expensive initial training to teach the model general language. Fine-tuning is secondary training to specialize it.

### Can I run fine-tuning on a consumer GPU?
Yes, using parameter-efficient fine-tuning (PEFT) methods like LoRA (Low-Rank Adaptation), which lock most weights and train only a small fraction.

## Related Terminology
- [models](https://spidits.com/ai-glossary/models)
- [llm](https://spidits.com/ai-glossary/llm)
- [inference](https://spidits.com/ai-glossary/inference)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*