TuningJob

The base model that is being tuned. See Supported models.

Output only. Time when the TuningJob was created.

Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.

Optional. The description of the TuningJob.

Tuning Spec for Distillation.

Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.

Output only. Time when the TuningJob entered any of the following JobStates: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED, JOB_STATE_EXPIRED.

Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Output only. Evaluation runs for the Tuning Job.

Output only. The Experiment associated with this TuningJob.

Tuning Spec for Full Fine Tuning.

Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

Output only. Identifier. Resource name of a TuningJob. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

Optional. Cloud Storage path to the directory where tuning job outputs are written to. This field is only available and required for open source models.

Tuning Spec for open sourced and third party Partner models.

Output only. The resource name of the PipelineJob associated with the TuningJob. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}.

Tuning Spec for Preference Optimization.

The pre-tuned model for continuous tuning.

Tuning Spec for Reinforcement Tuning.

Used to retain the full HTTP response.

The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the iam.serviceAccounts.actAs permission on this service account.

Output only. Time when the TuningJob for the first time entered the JOB_STATE_RUNNING state.

Output only. The detailed state of the job.

Tuning Spec for Supervised Fine Tuning.

Output only. The tuned model resources associated with this TuningJob.

Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters. For continuous tuning, tuned_model_display_name will by default use the same display name as the pre-tuned model. If a new display name is provided, the tuning job will create a new model instead of a new version.

Output only. The tuning data statistics associated with this TuningJob.

Output only. Tuning Job metadata.

Output only. The detail state of the tuning job (while the overall JobState is running).

Output only. Time when the TuningJob was most recently updated.

Tuning Spec for Veo LoRA Tuning.

Tuning Spec for Veo Tuning.