feat: add code samples for continuous tuning (#13594)

* feat: add code samples for continuous tuning

* feat: add code samples for continuous tuning

* Update continuous_tuning_create.py

* Update test_tuning_examples.py

* Update requirements.txt

* Rename continuous_tuning_create.py to tuning_with_pretuned_model.py

* Update test_tuning_examples.py

* Update tuning_with_pretuned_model.py

* Update genai/tuning/tuning_with_pretuned_model.py

Co-authored-by: Sampath Kumar <[email protected]>

---------

Co-authored-by: Sampath Kumar <[email protected]>

Author: yeyuanyyg

genai/tuning/requirements.txt

@@ -1 +1 @@
-google-genai==1.30.0
+google-genai==1.41.0

genai/tuning/test_tuning_examples.py

@@ -29,6 +29,7 @@
 import tuning_with_checkpoints_list_checkpoints
 import tuning_with_checkpoints_set_default_checkpoint
 import tuning_with_checkpoints_textgen_with_txt
+import tuning_with_pretuned_model
 
 
 GCS_OUTPUT_BUCKET = "python-docs-samples-tests"
@@ -306,3 +307,23 @@ def test_tuning_with_checkpoints_textgen_with_txt(mock_genai_client: MagicMock)
         call(model="test-endpoint-1", contents="Why is the sky blue?"),
         call(model="test-endpoint-2", contents="Why is the sky blue?"),
     ]
+
+
+@patch("google.genai.Client")
+def test_tuning_with_pretuned_model(mock_genai_client: MagicMock) -> None:
+    # Mock the API response
+    mock_tuning_job = types.TuningJob(
+        name="test-tuning-job",
+        experiment="test-experiment",
+        tuned_model=types.TunedModel(
+            model="test-model-2",
+            endpoint="test-endpoint"
+        )
+    )
+    mock_genai_client.return_value.tunings.tune.return_value = mock_tuning_job
+
+    response = tuning_with_pretuned_model.create_continuous_tuning_job(tuned_model_name="test-model", checkpoint_id="1")
+
+    mock_genai_client.assert_called_once_with(http_options=types.HttpOptions(api_version="v1beta1"))
+    mock_genai_client.return_value.tunings.tune.assert_called_once()
+    assert response == "test-tuning-job"

genai/tuning/tuning_with_pretuned_model.py

@@ -0,0 +1,78 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def create_continuous_tuning_job(tuned_model_name: str, checkpoint_id: str) -> str:
+    # [START googlegenaisdk_tuning_with_pretuned_model]
+    import time
+
+    from google import genai
+    from google.genai.types import HttpOptions, TuningDataset, CreateTuningJobConfig
+
+    # TODO(developer): Update and un-comment below line
+    # tuned_model_name = "projects/123456789012/locations/us-central1/models/1234567890@1"
+    # checkpoint_id = "1"
+
+    client = genai.Client(http_options=HttpOptions(api_version="v1beta1"))
+
+    training_dataset = TuningDataset(
+        gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini/text/sft_train_data.jsonl",
+    )
+    validation_dataset = TuningDataset(
+        gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini/text/sft_validation_data.jsonl",
+    )
+
+    tuning_job = client.tunings.tune(
+        base_model=tuned_model_name,  # Note: Using a Tuned Model
+        training_dataset=training_dataset,
+        config=CreateTuningJobConfig(
+            tuned_model_display_name="Example tuning job",
+            validation_dataset=validation_dataset,
+            pre_tuned_model_checkpoint_id=checkpoint_id,
+        ),
+    )
+
+    running_states = set([
+        "JOB_STATE_PENDING",
+        "JOB_STATE_RUNNING",
+    ])
+
+    while tuning_job.state in running_states:
+        print(tuning_job.state)
+        tuning_job = client.tunings.get(name=tuning_job.name)
+        time.sleep(60)
+
+    print(tuning_job.tuned_model.model)
+    print(tuning_job.tuned_model.endpoint)
+    print(tuning_job.experiment)
+    # Example response:
+    # projects/123456789012/locations/us-central1/models/1234567890@2
+    # projects/123456789012/locations/us-central1/endpoints/123456789012345
+    # projects/123456789012/locations/us-central1/metadataStores/default/contexts/tuning-experiment-2025010112345678
+
+    if tuning_job.tuned_model.checkpoints:
+        for i, checkpoint in enumerate(tuning_job.tuned_model.checkpoints):
+            print(f"Checkpoint {i + 1}: ", checkpoint)
+        # Example response:
+        # Checkpoint 1:  checkpoint_id='1' epoch=1 step=10 endpoint='projects/123456789012/locations/us-central1/endpoints/123456789000000'
+        # Checkpoint 2:  checkpoint_id='2' epoch=2 step=20 endpoint='projects/123456789012/locations/us-central1/endpoints/123456789012345'
+
+    # [END googlegenaisdk_tuning_with_pretuned_model]
+    return tuning_job.name
+
+
+if __name__ == "__main__":
+    pre_tuned_model_name = input("Pre-tuned model name: ")
+    pre_tuned_model_checkpoint_id = input("Pre-tuned model checkpoint id: ")
+    create_continuous_tuning_job(pre_tuned_model_name, pre_tuned_model_checkpoint_id)