feat: add code samples for preference tuning

genai/tuning/preference_tuning_job_create.py

@@ -0,0 +1,74 @@
+# 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_tuning_job() -> str:
+    # [START googlegenaisdk_preference_tuning_job_create]
+    import time
+
+    from google import genai
+    from google.genai.types import HttpOptions, CreateTuningJobConfig, TuningDataset
+
+    client = genai.Client(http_options=HttpOptions(api_version="v1"))
+
+    training_dataset = TuningDataset(
+        gcs_uri="gs://mybucket/preference_tuning/data/train_data.jsonl",
+    )
+    validation_dataset = TuningDataset(
+        gcs_uri="gs://mybucket/preference_tuning/data/validation_data.jsonl",
+    )
+
+    # Refer to https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini-use-continuous-tuning#google-gen-ai-sdk
+    # for example to continuous tune from SFT tuned model.
+    tuning_job = client.tunings.tune(
+        base_model="gemini-2.5-flash",
+        training_dataset=training_dataset,
+        config=CreateTuningJobConfig(
+            tuned_model_display_name="Example tuning job",
+            method="PREFERENCE_TUNING",
+            validation_dataset=validation_dataset,
+        ),
+    )
+
+    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@1
+    # 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_preference_tuning_job_create]
+    return tuning_job.name
+
+
+if __name__ == "__main__":
+    create_tuning_job()