diff --git a/vertexai/language_models/_language_models.py b/vertexai/language_models/_language_models.py
index 2076b56541..5d197aefc6 100644
--- a/vertexai/language_models/_language_models.py
+++ b/vertexai/language_models/_language_models.py
@@ -1996,7 +1996,7 @@ class TextEmbeddingInput:
     title: Optional[str] = None
 
 
-class TextEmbeddingModel(_LanguageModel):
+class _TextEmbeddingModel(_LanguageModel):
     """TextEmbeddingModel class calculates embeddings for the given texts.
 
     Examples::
@@ -2126,6 +2126,69 @@ async def get_embeddings_async(
         ]
 
 
+class _TunableTextEmbeddingModelMixin(_TunableModelMixin):
+    @classmethod
+    def get_tuned_model():
+        raise NotImplementedError(
+            "Use deploy_tuned_model instead to get the tuned model."
+        )
+
+    # IMPORTANT: Keep this method supported even if you end up deploying the tuned model as part of the tuning pipeline template.
+    @classmethod
+    def deploy_tuned_model(
+        cls,
+        tuned_model_name: str,
+        machine_type: Optional[str] = None,
+        accelerator: Optional[str] = None,
+        accelerator_count: Optional[int] = None,
+    ) -> "_LanguageModel":
+        """Loads the specified tuned language model.
+
+        Args:
+            tuned_model_name: Tuned model's resource name.
+            machine_type: Machine type. E.g., "a2-highgpu-1g". See also: https://cloud.google.com/vertex-ai/docs/training/configure-compute.
+            accelerator: Kind of accelerator. E.g., "NVIDIA_TESLA_A100". See also: https://cloud.google.com/vertex-ai/docs/training/configure-compute.
+            accelerator_count: Count of accelerators.
+
+        Returns:
+            Tuned `LanguageModel` object.
+        """
+        tuned_vertex_model = aiplatform.Model(tuned_model_name)
+        tuned_model_labels = tuned_vertex_model.labels
+
+        if _TUNING_BASE_MODEL_ID_LABEL_KEY not in tuned_model_labels:
+            raise ValueError(
+                f"The provided model {tuned_model_name} does not have a base model ID."
+            )
+
+        tuning_model_id = tuned_vertex_model.labels[_TUNING_BASE_MODEL_ID_LABEL_KEY]
+        tuned_model_deployments = tuned_vertex_model.gca_resource.deployed_models
+        if len(tuned_model_deployments) == 0:
+            # Deploying a model to an endpoint requires a resource quota.
+            endpoint_name = tuned_vertex_model.deploy(
+                machine_type=machine_type,
+                accelerator_type=accelerator,
+                accelerator_count=accelerator_count,
+            ).resource_name
+        else:
+            endpoint_name = tuned_model_deployments[0].endpoint
+
+        base_model_id = _get_model_id_from_tuning_model_id(tuning_model_id)
+        model_info = _model_garden_models._get_model_info(
+            model_id=base_model_id,
+            schema_to_class_map={cls._INSTANCE_SCHEMA_URI: cls},
+        )
+        model = model_info.interface_class(
+            model_id=base_model_id,
+            endpoint_name=endpoint_name,
+        )
+        return model
+
+
+class TextEmbeddingModel(_TextEmbeddingModel, _TunableTextEmbeddingModelMixin):
+    __module__ = "vertexai.language_models"
+
+
 class _PreviewTextEmbeddingModel(
     TextEmbeddingModel, _ModelWithBatchPredict, _CountTokensMixin
 ):