Model tuning is only supported in Vertex AI.
Create a tuning job
Start a supervised fine-tuning job:
from google.genai import types
model = 'gemini-2.5-flash'
training_dataset = types.TuningDataset(
# or gcs_uri=my_vertex_multimodal_dataset
gcs_uri='gs://your-gcs-bucket/your-tuning-data.jsonl',
)
tuning_job = client.tunings.tune(
base_model=model,
training_dataset=training_dataset,
config=types.CreateTuningJobConfig(
epoch_count=1,
tuned_model_display_name='test_dataset_examples model'
),
)
print(tuning_job)
GCS JSONL file
Provide a path to a JSONL file in Google Cloud Storage:
training_dataset = types.TuningDataset(
gcs_uri='gs://your-gcs-bucket/training-data.jsonl',
)
{"contents": [{"role": "user", "parts": [{"text": "What is AI?"}]}, {"role": "model", "parts": [{"text": "AI is..."}]}]}
{"contents": [{"role": "user", "parts": [{"text": "Explain ML"}]}, {"role": "model", "parts": [{"text": "ML is..."}]}]}
Vertex AI Multimodal Dataset
Use a Vertex AI Multimodal Dataset:
training_dataset = types.TuningDataset(
gcs_uri='projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID',
)
Get tuning job status
Retrieve the status of a tuning job:
tuning_job = client.tunings.get(name=tuning_job.name)
print(tuning_job)
print(tuning_job.state)
Poll for completion
Wait for a tuning job to complete:
import time
completed_states = set([
'JOB_STATE_SUCCEEDED',
'JOB_STATE_FAILED',
'JOB_STATE_CANCELLED',
])
while tuning_job.state not in completed_states:
print(tuning_job.state)
tuning_job = client.tunings.get(name=tuning_job.name)
time.sleep(10)
if tuning_job.state == 'JOB_STATE_SUCCEEDED':
print("Tuning completed successfully!")
else:
print(f"Tuning ended with state: {tuning_job.state}")
Use tuned models
Once tuning is complete, use the tuned model endpoint:
response = client.models.generate_content(
model=tuning_job.tuned_model.endpoint,
contents='why is the sky blue?',
)
print(response.text)
Get tuned model details
Retrieve information about a tuned model:
tuned_model = client.models.get(model=tuning_job.tuned_model.model)
print(tuned_model)
List tuned models
List all your tuned models:
for model in client.models.list(config={'page_size': 10, 'query_base': False}):
print(model)
pager = client.models.list(config={'page_size': 10, 'query_base': False})
print(pager.page_size)
print(pager[0])
pager.next_page()
print(pager[0])
Async listing
async for job in await client.aio.models.list(
config={'page_size': 10, 'query_base': False}
):
print(job)
async_pager = await client.aio.models.list(
config={'page_size': 10, 'query_base': False}
)
print(async_pager.page_size)
print(async_pager[0])
await async_pager.next_page()
print(async_pager[0])
Update tuned models
Update display name and description:
from google.genai import types
model = pager[0]
model = client.models.update(
model=model.name,
config=types.UpdateModelConfig(
display_name='my tuned model',
description='my tuned model description'
),
)
print(model)
List tuning jobs
List all tuning jobs:
for job in client.tunings.list(config={'page_size': 10}):
print(job)
Tuning configuration
The CreateTuningJobConfig supports:
- epoch_count - Number of training epochs
- tuned_model_display_name - Display name for the tuned model
- learning_rate - Learning rate for training
- batch_size - Training batch size
Best practices
- Use at least 100-500 high-quality training examples
- Format your data consistently
- Monitor the tuning job state regularly
- Test your tuned model before deploying to production
- Keep your training data in GCS for easy access
- Use validation data to prevent overfitting