diff --git a/src/llm_vm/completion/optimize.py b/src/llm_vm/completion/optimize.py
index af9625f2..9600e697 100644
--- a/src/llm_vm/completion/optimize.py
+++ b/src/llm_vm/completion/optimize.py
@@ -15,6 +15,80 @@
 # we need to package-ify so this works
 import llm_vm.completion.data_synthesis as data_synthesis
 import inspect
+from __future__ import annotations
+
+
+from typing import Optional
+
+
+from google.auth import default
+from google.cloud import aiplatform
+import pandas as pd
+import vertexai
+from vertexai.language_models import TextGenerationModel
+from vertexai.preview.language_models import TuningEvaluationSpec
+
+
+credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
+
+
+
+def tuning(
+    project_id: str,
+    location: str,
+    model_display_name: str,
+    training_data: pd.DataFrame | str,
+    train_steps: int = 10,
+    evaluation_dataset: Optional[str] = None,
+    tensorboard_instance_name: Optional[str] = None,
+) -> TextGenerationModel:
+    """Tune a new model, based on a prompt-response data.
+    "training_data" can be either the GCS URI of a file formatted in JSONL format
+    (for example: training_data=f'gs://{bucket}/{filename}.jsonl'), or a pandas
+    DataFrame. Each training example should be JSONL record with two keys, for
+    example:
+      {
+        "input_text": <input prompt>,
+        "output_text": <associated output>
+      },
+    or the pandas DataFame should contain two columns:
+      ['input_text', 'output_text']
+    with rows for each training example.
+    Args:
+      project_id: GCP Project ID, used to initialize vertexai
+      location: GCP Region, used to initialize vertexai
+      model_display_name: Customized Tuned LLM model name.
+      training_data: GCS URI of jsonl file or pandas dataframe of training data.
+      train_steps: Number of training steps to use when tuning the model.
+      evaluation_dataset: GCS URI of jsonl file of evaluation data.
+      tensorboard_instance_name: The full name of the existing Vertex AI TensorBoard instance:
+        projects/PROJECT_ID/locations/LOCATION_ID/tensorboards/TENSORBOARD_INSTANCE_ID
+        Note that this instance must be in the same region as your tuning job.
+    """
+    vertexai.init(project=project_id, location=location, credentials=credentials)
+    eval_spec = TuningEvaluationSpec(evaluation_data=evaluation_dataset)
+    eval_spec.tensorboard = aiplatform.Tensorboard(
+        tensorboard_name=tensorboard_instance_name
+    )
+    model = TextGenerationModel.from_pretrained("text-bison@001")
+
+    model.tune_model(
+        training_data=training_data,
+        # Optional:
+        model_display_name=model_display_name,
+        train_steps=train_steps,
+        tuning_job_location="europe-west4",
+        tuned_model_location=location,
+        tuning_evaluation_spec=eval_spec,
+    )
+
+    print(model._job.status)
+
+    return model
+
+
+if __name__ == "__main__":
+    tuning()
 
 
 job_id = None # we want to be able to cancel a fine_tune if you kill the program