Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window. Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.

Method: projects.locations.tuningJobs.get

Gets a TuningJob.

Endpoint

get


                https://{service-endpoint}/v1beta1/{name}

Where {service-endpoint} is one of the supported service endpoints.

Path parameters

name string

Required. The name of the TuningJob resource. Format: projects/{project}/locations/{location}/tuningJobs/{tuningJob}

Request body

The request body must be empty.

Example request

Python

import vertexai
from vertexai.preview.tuning import sft

# TODO(developer): Update project, location
vertexai.init(project=PROJECT_ID, location=LOCATION)

tuning_job_id = "4982013113894174720"
response = sft.SupervisedTuningJob(
    f"projects/{PROJECT_ID}/locations/{LOCATION}/tuningJobs/{tuning_job_id}"
)

print(response)gemini_tuning.py

Response body

If successful, the response body contains an instance of TuningJob.