diff --git a/README.md b/README.md
index 00674fb442f..2e6cd40a9c8 100644
--- a/README.md
+++ b/README.md
@@ -245,6 +245,7 @@ Here is an example of Accuracy Aware Quantization pipeline where model weights a
 
 ```python
 import nncf
+import nncf.torch
 import torch
 from torchvision import datasets, models
 
@@ -271,7 +272,7 @@ quantized_model = nncf.quantize(model, calibration_dataset)
 # Save quantization modules and the quantized model parameters
 checkpoint = {
     'state_dict': model.state_dict(),
-    'nncf_config': model.nncf.get_config(),
+    'nncf_config': nncf.torch.get_config(model),
     ... # the rest of the user-defined objects to save
 }
 torch.save(checkpoint, path_to_checkpoint)
diff --git a/docs/usage/training_time_compression/quantization_aware_training/Usage.md b/docs/usage/training_time_compression/quantization_aware_training/Usage.md
index 79033ac09a6..aed7dfafba2 100644
--- a/docs/usage/training_time_compression/quantization_aware_training/Usage.md
+++ b/docs/usage/training_time_compression/quantization_aware_training/Usage.md
@@ -75,17 +75,19 @@ ov_quantized_model = ov.convert_model(stripped_model)
 
 The complete information about compression is defined by a compressed model and a NNCF config.
 The model characterizes the weights and topology of the network. The NNCF config - how to restore additional modules introduced by NNCF.
-The NNCF config can be obtained by `quantized_model.nncf.get_config()` on saving and passed to the
+The NNCF config can be obtained by `nncf.torch.get_config` on saving and passed to the
 `nncf.torch.load_from_config` helper function to load additional modules from the given NNCF config.
 The quantized model saving allows to load quantized modules to the target model in a new python process and
 requires only example input for the target module, corresponding NNCF config and the quantized model state dict.
 
 ```python
+import nncf.torch
+
 # save part
 quantized_model = nncf.quantize(model, calibration_dataset)
 checkpoint = {
-    'state_dict':quantized_model.state_dict(),
-    'nncf_config': quantized_model.nncf.get_config(),
+    'state_dict': quantized_model.state_dict(),
+    'nncf_config': nncf.torch.get_config(quantized_model),
     ...
 }
 torch.save(checkpoint, path)
diff --git a/examples/quantization_aware_training/torch/resnet18/main.py b/examples/quantization_aware_training/torch/resnet18/main.py
index a8a96d041c3..e1bdec21b27 100644
--- a/examples/quantization_aware_training/torch/resnet18/main.py
+++ b/examples/quantization_aware_training/torch/resnet18/main.py
@@ -278,11 +278,11 @@ def transform_fn(data_item):
         print(f"Train epoch: {epoch}")
         train_epoch(train_loader, quantized_model, criterion, optimizer, device=device)
         acc1_int8 = validate(val_loader, quantized_model, device)
-        print(f"Accyracy@1 of INT8 model after {epoch} epoch finetuning: {acc1_int8:.3f}")
+        print(f"Accuracy@1 of INT8 model after {epoch} epoch finetuning: {acc1_int8:.3f}")
         # Save the compression checkpoint for model with the best accuracy metric.
         if acc1_int8 > acc1_int8_best:
             state_dict = quantized_model.state_dict()
-            compression_config = quantized_model.nncf.get_config()
+            compression_config = nncf.torch.get_config(quantized_model)
             torch.save(
                 {
                     "model_state_dict": state_dict,
diff --git a/nncf/torch/__init__.py b/nncf/torch/__init__.py
index 4627b8d3f8f..d8cf9aee6a6 100644
--- a/nncf/torch/__init__.py
+++ b/nncf/torch/__init__.py
@@ -50,6 +50,7 @@
 from nncf.torch.model_creation import is_wrapped_model
 from nncf.torch.model_creation import wrap_model
 from nncf.torch.model_creation import load_from_config
+from nncf.torch.model_creation import get_config
 from nncf.torch.checkpoint_loading import load_state
 from nncf.torch.initialization import register_default_init_args
 from nncf.torch.layers import register_module
diff --git a/nncf/torch/model_creation.py b/nncf/torch/model_creation.py
index e64c8b48818..15bab5748b3 100644
--- a/nncf/torch/model_creation.py
+++ b/nncf/torch/model_creation.py
@@ -28,6 +28,8 @@
 from nncf.config.extractors import has_input_info_field
 from nncf.config.telemetry_extractors import CompressionStartedFromConfig
 from nncf.experimental.common.check_feature import is_experimental_torch_tracing_enabled
+from nncf.experimental.torch2.function_hook.serialization import get_config as pt2_get_config
+from nncf.experimental.torch2.function_hook.serialization import load_from_config as pt2_load_from_config
 from nncf.telemetry import tracked_function
 from nncf.telemetry.events import NNCF_PT_CATEGORY
 from nncf.telemetry.extractors import FunctionCallTelemetryExtractor
@@ -397,18 +399,43 @@ def is_wrapped_model(model: Any) -> bool:
         FunctionCallTelemetryExtractor("nncf.torch.load_from_config"),
     ],
 )
-def load_from_config(model: torch.nn.Module, config: Dict[str, Any], example_input: Any) -> NNCFNetwork:
+def load_from_config(model: Module, config: Dict[str, Any], example_input: Optional[Any] = None) -> Module:
     """
-    Wraps given model to a NNCFNetwork and recovers additional modules from given NNCFNetwork config.
+    Wraps given model and recovers additional modules from given config.
     Does not recover additional modules weights as they are located in a corresponded state_dict.
 
     :param model: PyTorch model.
     :param config: NNCNetwork config.
     :param example_input: An example input that will be used for model tracing. A tuple is interpreted
         as an example input of a set of non keyword arguments, and a dict as an example input of a set
-        of keywords arguments.
-    :return: NNCFNetwork builded from given model with additional modules recovered from given NNCFNetwork config.
+        of keywords arguments. Required with enabled legacy tracing mode.
+    :return: Wrapped model with additional modules recovered from given config.
     """
+    if is_experimental_torch_tracing_enabled():
+        return pt2_load_from_config(model, config)
+
+    if example_input is None:
+        msg = "The 'example_input' parameter must be specified."
+        raise nncf.InternalError(msg)
+
     nncf_network = wrap_model(model, example_input, trace_parameters=config[NNCFNetwork.TRACE_PARAMETERS_KEY])
     transformation_layout = deserialize_transformations(config)
     return PTModelTransformer(nncf_network).transform(transformation_layout)
+
+
+@tracked_function(
+    NNCF_PT_CATEGORY,
+    [
+        FunctionCallTelemetryExtractor("nncf.torch.get_config"),
+    ],
+)
+def get_config(model: Module) -> Dict[str, Any]:
+    """
+    Returns the configuration object of the compressed model.
+
+    :param model: The compressed model.
+    :return: The configuration object of the compressed model.
+    """
+    if is_experimental_torch_tracing_enabled():
+        return pt2_get_config(model)
+    return model.nncf.get_config()
diff --git a/tests/torch2/function_hook/test_serialization.py b/tests/torch2/function_hook/test_serialization.py
index 901e45dc8b0..4b72dff89e7 100644
--- a/tests/torch2/function_hook/test_serialization.py
+++ b/tests/torch2/function_hook/test_serialization.py
@@ -19,8 +19,8 @@
 from nncf.experimental.torch2.function_hook import register_post_function_hook
 from nncf.experimental.torch2.function_hook import register_pre_function_hook
 from nncf.experimental.torch2.function_hook import wrap_model
-from nncf.experimental.torch2.function_hook.serialization import get_config
-from nncf.experimental.torch2.function_hook.serialization import load_from_config
+from nncf.torch import get_config
+from nncf.torch import load_from_config
 from tests.torch2.function_hook.helpers import HookWithState
 from tests.torch2.function_hook.helpers import SimpleModel