Quick definition
LoRA adds low-rank adapters for efficient fine-tuning.
- Category: Training
- Focus: model adaptation
- Used in: Adapting a base model to your domain or style.
What it means
It updates small matrices instead of all model weights. In training workflows, lora often shapes model adaptation.
How it works
Training adapts models through fine-tuning or preference optimization. It uses curated datasets and evaluation loops.
Why it matters
Training methods tailor models to your domain and use case.
Common use cases
- Adapting a base model to your domain or style.
- Improving instruction following for specific tasks.
- Reducing errors with better training data.
Example
Fine-tune a large model with a small LoRA file.
Pitfalls and tips
Low-quality data can degrade performance. Keep datasets clean, representative, and well-labeled.
In BoltAI
In BoltAI, this is referenced when discussing model customization.
