common-fine-tuning-method.md

Method Name	Description	Use Case / Notes
Full Fine-Tuning	Train all weights of the pretrained model on your dataset	Best for large datasets, very GPU intensive
Feature Extraction	Freeze the backbone (encoder) and train only the decoder / head	Good for small datasets, low GPU
LoRA (Low-Rank Adaptation)	Adds small trainable adapter layers to pretrained attention layers	Extremely memory-efficient, works on mini datasets
DreamBooth	Fine-tune Stable Diffusion to generate custom subjects / styles	Specialized for image personalization
Adapter Tuning	Insert small adapter modules in transformer layers	Similar to LoRA but more modular
Prompt Tuning / Prefix Tuning	Train embeddings / tokens without changing main model weights	Works well for text & multimodal models