Create common-fine-tuning-method.md

common-fine-tuning-method.md

@@ -0,0 +1,8 @@
+| 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            |