Add documentation for PEFT (LoRA & DoRA) (#4596)

Co-authored-by: Vladislav Sovrasov <[email protected]>

Author: gyuilLim

docs/source/guide/tutorials/advanced/index.rst

@@ -7,7 +7,7 @@ Advanced Tutorials
    configuration
    huggingface_model
    multi_gpu
-   low_rank_adaptation
+   peft
    torch_compile
 
 .. Once we have enough material, we might need to categorize these into `data`, `model learning` sections.

docs/source/guide/tutorials/advanced/peft.rst

@@ -0,0 +1,107 @@
+PEFT: Parameter-Efficient Fine-Tuning (LoRA & DoRA) for Classification
+======================================================================
+
+.. note::
+
+    PEFT (LoRA, DoRA) is only supported for VisionTransformer models.
+    See the method in otx.backend.native.models.classification.utils.peft
+
+
+Overview
+--------
+
+OpenVINO™ Training Extensions supports Parameter-Efficient Fine-Tuning (PEFT) for Transformer classifiers via Low Rank Adaptation (LoRA) and Weight-Decomposed Low-Rank Adaptation (DoRA).
+These methods adapt pre-trained models with a small number of additional parameters instead of fully fine-tuning all weights.
+
+Benefits
+--------
+
+- **Efficiency**: Minimal extra parameters and faster adaptation.
+- **Performance**: Competitive accuracy compared to full fine-tuning.
+- **Flexibility**: Apply LoRA or DoRA selectively to model components.
+
+Supported
+---------
+
+- **Backbones**: Vision Transformer family (e.g., DINOv2)
+- **Tasks**: Multiclass, Multi-label, Hierarchical Label Classification
+
+How to Use PEFT in OpenVINO™ Training Extensions
+--------------------------------------------------
+
+.. tab-set::
+
+   .. tab-item:: API
+
+      .. code-block:: python
+
+         from training_extensions.src.otx.backend.native.models.classification.multiclass_models.vit import VisionTransformerMulticlassCls
+
+         # Choose one: "lora" or "dora"
+         model = VisionTransformerForMulticlassCls(..., peft="lora")
+
+   .. tab-item:: CLI
+
+      .. code-block:: bash
+
+         (otx) $ otx train ... --model.peft dora
+
+   .. tab-item:: YAML
+
+      .. code-block:: yaml
+
+         task: MULTI_CLASS_CLS
+         model:
+            class_path: otx.backend.native.models.classification.multiclass_models.vit.VisionTransformerMulticlassCls
+            init_args:
+               label_info: 1000
+               model_name: "dinov2-small"
+               peft: "dora"
+
+               optimizer:
+                  class_path: torch.optim.AdamW
+                  init_args:
+                     lr: 0.0001
+                     weight_decay: 0.05
+
+Alternative
+-----------
+
+- **Linear Fine-Tuning**: Train only the classification head while keeping all backbone frozen.
+  This approach works with *all* classification backbones.
+
+How to Use Linear Fine-Tuning
+-----------------------------
+
+.. tab-set::
+
+   .. tab-item:: API
+
+      .. code-block:: python
+
+         from training_extensions.src.otx.backend.native.models.classification.multiclass_models.vit import VisionTransformerMulticlassCls
+
+         # Linear FT = freeze_backbone=True, no PEFT
+         model = VisionTransformerMulticlassCls(
+             ...,
+             freeze_backbone=True,
+         )
+
+   .. tab-item:: CLI
+
+      .. code-block:: bash
+
+         (otx) $ otx train ... --model.freeze_backbone true
+
+   .. tab-item:: YAML
+
+      .. code-block:: yaml
+
+         task: MULTI_CLASS_CLS
+         model:
+            class_path: otx.backend.native.models.classification.multiclass_models.vit.VisionTransformerMulticlassCls
+            init_args:
+               label_info: 1000
+               model_name: "dinov2-small"
+               peft: ""
+               freeze_backbone: true