> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/googleapis/python-genai/llms.txt
> Use this file to discover all available pages before exploring further.

# tunings.get

> Retrieve information about a tuning job

## Method

```python theme={null}
client.tunings.get(
    name: str,
    config: Optional[GetTuningJobConfig] = None
) -> TuningJob
```

Retrieves detailed information about a specific tuning job by its resource name.

<ParamField path="name" type="string" required>
  The resource name of the tuning job. Format:

  * Vertex AI: `projects/{project}/locations/{location}/tuningJobs/{job_id}`
  * Gemini API: `tunedModels/{model_id}`
</ParamField>

<ParamField path="config" type="GetTuningJobConfig">
  Optional configuration for the request

  <Expandable title="Configuration Fields">
    <ParamField path="http_options" type="HttpOptions">
      Custom HTTP options for the request
    </ParamField>
  </Expandable>
</ParamField>

## Response

<ResponseField name="name" type="string">
  The resource name of the tuning job
</ResponseField>

<ResponseField name="state" type="JobState">
  Current state of the tuning job:

  * `JOB_STATE_QUEUED`
  * `JOB_STATE_PENDING`
  * `JOB_STATE_RUNNING`
  * `JOB_STATE_SUCCEEDED`
  * `JOB_STATE_FAILED`
  * `JOB_STATE_CANCELLED`
</ResponseField>

<ResponseField name="create_time" type="string">
  ISO 8601 timestamp when the job was created
</ResponseField>

<ResponseField name="start_time" type="string">
  ISO 8601 timestamp when the job started
</ResponseField>

<ResponseField name="end_time" type="string">
  ISO 8601 timestamp when the job completed
</ResponseField>

<ResponseField name="update_time" type="string">
  ISO 8601 timestamp of the last update
</ResponseField>

<ResponseField name="base_model" type="string">
  The base model that is being tuned
</ResponseField>

<ResponseField name="tuned_model" type="TunedModel">
  Information about the resulting tuned model (available after completion)

  <Expandable title="TunedModel Fields">
    <ResponseField name="model" type="string">
      The resource name of the tuned model
    </ResponseField>

    <ResponseField name="endpoint" type="string">
      The endpoint to use for the tuned model
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="error" type="object">
  Error information if the job failed
</ResponseField>

<ResponseField name="description" type="string">
  Description of the tuning job (Vertex AI only)
</ResponseField>

<ResponseField name="tuning_data_stats" type="object">
  Statistics about the training data (Vertex AI only)
</ResponseField>

## Usage

### Check Job Status

```python theme={null}
from google import genai

client = genai.Client(api_key='your-api-key')

# Get tuning job details
tuning_job = client.tunings.get(
    name='projects/my-project/locations/us-central1/tuningJobs/123456'
)

print(f"Job: {tuning_job.name}")
print(f"State: {tuning_job.state}")
print(f"Created: {tuning_job.create_time}")

if tuning_job.state == 'JOB_STATE_SUCCEEDED':
    print(f"Tuned model: {tuning_job.tuned_model.model}")
```

### Monitor Job Progress

```python theme={null}
import time
from google.genai import types

job_name = 'projects/my-project/locations/us-central1/tuningJobs/123456'

while True:
    tuning_job = client.tunings.get(name=job_name)
    
    print(f"Current state: {tuning_job.state}")
    
    # Check if job is complete
    if tuning_job.state in [
        types.JobState.JOB_STATE_SUCCEEDED,
        types.JobState.JOB_STATE_FAILED,
        types.JobState.JOB_STATE_CANCELLED
    ]:
        break
    
    # Wait before checking again
    time.sleep(60)

if tuning_job.state == types.JobState.JOB_STATE_SUCCEEDED:
    print(f"Success! Tuned model: {tuning_job.tuned_model.model}")
else:
    print(f"Job ended with state: {tuning_job.state}")
    if tuning_job.error:
        print(f"Error: {tuning_job.error}")
```

### Handle Job Completion

```python theme={null}
from google.genai import types

tuning_job = client.tunings.get(name=job_name)

# Check job status and take action
if tuning_job.state == types.JobState.JOB_STATE_SUCCEEDED:
    # Use the tuned model
    model = tuning_job.tuned_model.model
    response = client.models.generate_content(
        model=model,
        contents='Test the tuned model'
    )
    print(response.text)
    
elif tuning_job.state == types.JobState.JOB_STATE_FAILED:
    print(f"Tuning failed: {tuning_job.error}")
    
elif tuning_job.state == types.JobState.JOB_STATE_RUNNING:
    elapsed = tuning_job.update_time - tuning_job.start_time
    print(f"Job still running, elapsed time: {elapsed}")
```

## See Also

* [tunings.tune](/api/tunings/tune) - Create a new tuning job
* [tunings.list](/api/tunings/list) - List all tuning jobs
* [tunings.cancel](/api/tunings/cancel) - Cancel a running job