Added documentation

docs/user-guide/training.rst

@@ -280,3 +280,65 @@ finished training. However if the user wants to restart the model from a
 specific point they can do this by setting
 ``config.hardware.files.warm_start`` to be the checkpoint they want to
 restart from..
+
+*******************
+ Transfer Learning
+*******************
+
+Transfer learning allows the model to reuse knowledge from a previously
+trained checkpoint. This is particularly useful when the new task is
+related to the old one, enabling faster convergence and often improving
+model performance.
+
+To enable transfer learning, set the config.training.transfer_learning
+flag to True in the configuration file.
+
+.. code:: yaml
+
+   training:
+      # start the training from a checkpoint of a previous run
+      fork_run_id: '51a97d40a49e48d284494a3b5d87ef2b'
+      load_weights_only: True
+      transfer_learning: True
+
+When this flag is active and a checkpoint path is specified in
+config.hardware.files.warm_start or self.last_checkpoint, the system
+loads the pre-trained weights using the `transfer_learning_loading`
+function. This approach ensures only compatible weights are loaded and
+mismatched layers are handled appropriately.
+
+For example, transfer learning might be used to adapt a weather
+forecasting model trained on one geographic region to another region
+with similar characteristics.
+
+****************
+ Model Freezing
+****************
+
+Model freezing is a technique where specific parts (submodules) of a
+model are excluded from training. This is useful when certain parts of
+the model have been sufficiently trained or should remain unchanged for
+the current task.
+
+To specify which submodules to freeze, use the
+config.training.submodules_to_freeze field in the configuration. List
+the names of submodules to be frozen. During model initialization, these
+submodules will have their parameters frozen, ensuring they are not
+updated during training.
+
+For example with the following configuration, the processor will be
+frozen and only the encoder and decoder will be trained:
+
+.. code:: yaml
+
+   training:
+      # start the training from a checkpoint of a previous run
+      fork_run_id: '51a97d40a49e48d284494a3b5d87ef2b'
+      load_weights_only: True
+
+      submodules_to_freeze:
+         - processor
+
+Freezing can be particularly beneficial in scenarios such as fine-tuning
+when only specific components (e.g., the encoder, the decoder) need to
+adapt to a new task while keeping others (e.g., the processor) fixed.