diff --git a/llama_stack/providers/remote/post_training/nvidia/post_training.py b/llama_stack/providers/remote/post_training/nvidia/post_training.py
index 409818cb3..d839ffd6f 100644
--- a/llama_stack/providers/remote/post_training/nvidia/post_training.py
+++ b/llama_stack/providers/remote/post_training/nvidia/post_training.py
@@ -224,7 +224,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
 
         Parameters:
             training_config: TrainingConfig - Configuration for training
-            model: str - Model identifier
+            model: str - NeMo Customizer configuration name
             algorithm_config: Optional[AlgorithmConfig] - Algorithm-specific configuration
             checkpoint_dir: Optional[str] - Directory containing model checkpoints, ignored atm
             job_uuid: str - Unique identifier for the job, ignored atm
@@ -299,9 +299,6 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
 
             User is informed about unsupported parameters via warnings.
         """
-        # Map model to nvidia model name
-        # See `_MODEL_ENTRIES` for supported models
-        nvidia_model = self.get_provider_model_id(model)
 
         # Check for unsupported method parameters
         unsupported_method_params = []
@@ -347,7 +344,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
 
         # Prepare base job configuration
         job_config = {
-            "config": nvidia_model,
+            "config": model,
             "dataset": {
                 "name": training_config["data_config"]["dataset_id"],
                 "namespace": self.config.dataset_namespace,
diff --git a/tests/unit/providers/nvidia/test_parameters.py b/tests/unit/providers/nvidia/test_parameters.py
index ea12122a0..cc33f7609 100644
--- a/tests/unit/providers/nvidia/test_parameters.py
+++ b/tests/unit/providers/nvidia/test_parameters.py
@@ -131,7 +131,7 @@ class TestNvidiaParameters(unittest.TestCase):
 
     def test_required_parameters_passed(self):
         """Test scenario 2: When required parameters are passed."""
-        required_model = "meta-llama/Llama-3.1-8B-Instruct"
+        required_model = "meta/llama-3.2-1b-instruct@v1.0.0+L40"
         required_dataset_id = "required-dataset"
         required_job_uuid = "required-job"
 
@@ -190,7 +190,7 @@ class TestNvidiaParameters(unittest.TestCase):
         self.mock_make_request.assert_called_once()
         call_args = self.mock_make_request.call_args
 
-        assert call_args[1]["json"]["config"] == "meta/llama-3.1-8b-instruct"
+        assert call_args[1]["json"]["config"] == required_model
         assert call_args[1]["json"]["dataset"]["name"] == required_dataset_id
 
     def test_unsupported_parameters_warning(self):
diff --git a/tests/unit/providers/nvidia/test_supervised_fine_tuning.py b/tests/unit/providers/nvidia/test_supervised_fine_tuning.py
index 319011be3..97ca02fba 100644
--- a/tests/unit/providers/nvidia/test_supervised_fine_tuning.py
+++ b/tests/unit/providers/nvidia/test_supervised_fine_tuning.py
@@ -165,7 +165,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
             training_job = self.run_async(
                 self.adapter.supervised_fine_tune(
                     job_uuid="1234",
-                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    model="meta/llama-3.2-1b-instruct@v1.0.0+L40",
                     checkpoint_dir="",
                     algorithm_config=algorithm_config,
                     training_config=convert_pydantic_to_json_value(training_config),
@@ -184,7 +184,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
             "POST",
             "/v1/customization/jobs",
             expected_json={
-                "config": "meta/llama-3.1-8b-instruct",
+                "config": "meta/llama-3.2-1b-instruct@v1.0.0+L40",
                 "dataset": {"name": "sample-basic-test", "namespace": "default"},
                 "hyperparameters": {
                     "training_type": "sft",
@@ -219,7 +219,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
             self.run_async(
                 self.adapter.supervised_fine_tune(
                     job_uuid="1234",
-                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    model="meta/llama-3.2-1b-instruct@v1.0.0+L40",
                     checkpoint_dir="",
                     algorithm_config=algorithm_config,
                     training_config=convert_pydantic_to_json_value(training_config),