diff --git a/template/steps/deploying/save_model.py b/template/steps/deploying/save_model.py
index 04aef8a..8b257e0 100644
--- a/template/steps/deploying/save_model.py
+++ b/template/steps/deploying/save_model.py
@@ -2,7 +2,6 @@
 
 
 from zenml import get_step_context, step
-from zenml.client import Client
 from zenml.logger import get_logger
 
 # Initialize logger
@@ -25,8 +24,6 @@ def save_model_to_deploy():
     """
     ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###
     pipeline_extra = get_step_context().pipeline_run.config.extra
-    zenml_client = Client()
-
     logger.info(
         f" Loading latest version of the model for stage {pipeline_extra['target_env']}..."
     )
@@ -34,8 +31,8 @@ def save_model_to_deploy():
     latest_version = get_step_context().model_version._get_model_version()
 
     # Load model and tokenizer from Model Control Plane
-    model = latest_version.get_model_object(name="model").load()
-    tokenizer = latest_version.get_model_object(name="tokenizer").load()
+    model = latest_version.load_artifact(name="model")
+    tokenizer = latest_version.load_artifact(name="tokenizer")
     # Save the model and tokenizer locally
     model_path = "./gradio/model"  # replace with the actual path
     tokenizer_path = "./gradio/tokenizer"  # replace with the actual path
diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_get_metrics.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_get_metrics.py{% endif %}
index 371c08a..c30e286 100644
--- a/template/steps/promotion/{% if metric_compare_promotion %}promote_get_metrics.py{% endif %}	
+++ b/template/steps/promotion/{% if metric_compare_promotion %}promote_get_metrics.py{% endif %}	
@@ -37,7 +37,7 @@ def promote_get_metrics(
     # Get current model version metric in current run
     model_version = get_step_context().model_version
     current_version = model_version._get_model_version()
-    current_metrics = current_version.get_model_object(name="model").metadata["metrics"].value
+    current_metrics = current_version.get_model_artifact("model").run_metadata["metrics"].value
     logger.info(f"Current model version metrics are {current_metrics}")
 
     # Get latest saved model version metric in target environment
@@ -49,7 +49,7 @@ def promote_get_metrics(
     except KeyError:
         latest_version = None
     if latest_version:
-        latest_metrics = current_version.get_model_object(name="model").metadata["metrics"].value
+        latest_metrics = current_version.get_model_artifact("model").run_metadata["metrics"].value
         logger.info(f"Current model version metrics are {latest_metrics}")
     else:
         logger.info("No currently promoted model version found.")
diff --git a/template/steps/training/model_trainer.py b/template/steps/training/model_trainer.py
index fd9142e..d000507 100644
--- a/template/steps/training/model_trainer.py
+++ b/template/steps/training/model_trainer.py
@@ -13,11 +13,10 @@
     TrainingArguments,
     AutoModelForSequenceClassification,
 )
-from zenml import log_artifact_metadata, step
+from zenml import ArtifactConfig, log_artifact_metadata, step
 from zenml.client import Client
 from zenml.integrations.mlflow.experiment_trackers import MLFlowExperimentTracker
 from zenml.logger import get_logger
-from zenml.model import ModelArtifactConfig
 from utils.misc import compute_metrics
 
 # Initialize logger
@@ -47,7 +46,7 @@ def model_trainer(
     eval_batch_size: Optional[int] = 16,
     weight_decay: Optional[float] = 0.01,
     mlflow_model_name: Optional[str] = "sentiment_analysis",
-) -> Tuple[Annotated[PreTrainedModel, "model", ModelArtifactConfig(overwrite=True)], Annotated[PreTrainedTokenizerBase, "tokenizer", ModelArtifactConfig(overwrite=True)]]:
+) -> Tuple[Annotated[PreTrainedModel, ArtifactConfig(name="model", is_model_artifact=True)], Annotated[PreTrainedTokenizerBase, ArtifactConfig(name="tokenizer", is_model_artifact=True)]]:
     """
     Configure and train a model on the training dataset.
 
@@ -136,7 +135,10 @@ def model_trainer(
     eval_results = trainer.evaluate(metric_key_prefix="")
 
     # Log the evaluation results in model control plane
-    log_artifact_metadata(output_name="model", metrics=eval_results)
+    log_artifact_metadata(
+        metadata={"metrics": eval_results},
+        artifact_name="model",
+    )
     ### YOUR CODE ENDS HERE ###
 
     return model, tokenizer
diff --git a/template/utils/misc.py b/template/utils/misc.py
index 7125f99..f8d326f 100644
--- a/template/utils/misc.py
+++ b/template/utils/misc.py
@@ -1,6 +1,6 @@
 # {% include 'template/license_header' %}
 
-from typing import Dict, Tuple, List
+from typing import Dict, List, Tuple
 
 import numpy as np
 from datasets import load_metric
diff --git a/tests/test_template.py b/tests/test_template.py
index 43574d7..3e8defe 100644
--- a/tests/test_template.py
+++ b/tests/test_template.py
@@ -47,7 +47,6 @@ def generate_and_run_project(
     cloud_of_choice: str = "gcp",
     dataset: str = "airline_reviews",
     zenml_model_name: str = "sentiment_analysis",
-
 ):
     """Generate and run the starter project with different options."""
 
@@ -162,6 +161,7 @@ def test_latest_promotion(
         tmp_path_factory=tmp_path_factory, metric_compare_promotion=False
     )
 
+
 def test_production_environment(
     clean_zenml_client,
     tmp_path_factory: pytest.TempPathFactory,