REST Resource: projects.locations.tuningJobs

Resource: TuningJob

Represents a TuningJob that runs with Google owned models.

Fields
name string

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

tunedModelDisplayName string

Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.

description string

Optional. The description of the TuningJob.

customBaseModel string

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 baseModel 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.

state enum (JobState)

Output only. The detailed state of the job.

tuningJobState enum (TuningJobState)

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

createTime string (Timestamp format)

Output only. time when the TuningJob was created.

Uses RFC 3339, where generated output will always be Z-normalized and uses 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

startTime string (Timestamp format)

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

Uses RFC 3339, where generated output will always be Z-normalized and uses 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

endTime string (Timestamp format)

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

Uses RFC 3339, where generated output will always be Z-normalized and uses 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

updateTime string (Timestamp format)

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

Uses RFC 3339, where generated output will always be Z-normalized and uses 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

error object (Status)

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

labels map (key: string, value: string)

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.

experiment string

Output only. The Experiment associated with this TuningJob.

tunedModel object (TunedModel)

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

tuningDataStats object (TuningDataStats)

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

pipelineJob
(deprecated) string

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

encryptionSpec object (EncryptionSpec)

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.

serviceAccount string

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.

outputUri string

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.

evaluateDatasetRuns[] object (EvaluateDatasetRun)

Output only. Evaluation runs for the Tuning Job.

satisfiesPzs boolean

Output only. reserved for future use.

satisfiesPzi boolean

Output only. reserved for future use.

source_model Union type
source_model can be only one of the following:
baseModel string

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

preTunedModel object (PreTunedModel)

The pre-tuned model for continuous tuning.

tuning_spec Union type
tuning_spec can be only one of the following:
supervisedTuningSpec object (SupervisedTuningSpec)

Tuning Spec for Supervised Fine Tuning.

distillationSpec object (DistillationSpec)

Tuning Spec for Distillation.

partnerModelTuningSpec object (PartnerModelTuningSpec)

Tuning Spec for open sourced and third party Partner models.

preferenceOptimizationSpec object (PreferenceOptimizationSpec)

Tuning Spec for Preference Optimization.

veoTuningSpec object (VeoTuningSpec)

Tuning Spec for Veo Tuning.

JSON representation 
{
  "name": string,
  "tunedModelDisplayName": string,
  "description": string,
  "customBaseModel": string,
  "state": enum (JobState),
  "tuningJobState": enum (TuningJobState),
  "createTime": string,
  "startTime": string,
  "endTime": string,
  "updateTime": string,
  "error": {
    object (Status)
  },
  "labels": {
    string: string,
    ...
  },
  "experiment": string,
  "tunedModel": {
    object (TunedModel)
  },
  "tuningDataStats": {
    object (TuningDataStats)
  },
  "pipelineJob": string,
  "encryptionSpec": {
    object (EncryptionSpec)
  },
  "serviceAccount": string,
  "outputUri": string,
  "evaluateDatasetRuns": [
    {
      object (EvaluateDatasetRun)
    }
  ],
  "satisfiesPzs": boolean,
  "satisfiesPzi": boolean,

  // source_model
  "baseModel": string,
  "preTunedModel": {
    object (PreTunedModel)
  }
  // Union type

  // tuning_spec
  "supervisedTuningSpec": {
    object (SupervisedTuningSpec)
  },
  "distillationSpec": {
    object (DistillationSpec)
  },
  "partnerModelTuningSpec": {
    object (PartnerModelTuningSpec)
  },
  "preferenceOptimizationSpec": {
    object (PreferenceOptimizationSpec)
  },
  "veoTuningSpec": {
    object (VeoTuningSpec)
  }
  // Union type
}

PreTunedModel

A pre-tuned model for continuous tuning.

Fields
tunedModelName string

The resource name of the Model. E.g., a model resource name with a specified version id or alias:

projects/{project}/locations/{location}/models/{model}@{versionId}

projects/{project}/locations/{location}/models/{model}@{alias}

Or, omit the version id to use the default version:

projects/{project}/locations/{location}/models/{model}

checkpointId string

Optional. The source checkpoint id. If not specified, the default checkpoint will be used.

baseModel string

Output only. The name of the base model this PreTunedModel was tuned from.

JSON representation 
{
  "tunedModelName": string,
  "checkpointId": string,
  "baseModel": string
}

SupervisedTuningSpec

Tuning Spec for Supervised Tuning for first party models.

Fields
trainingDatasetUri string

Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

validationDatasetUri string

Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

hyperParameters object (SupervisedHyperParameters)

Optional. Hyperparameters for SFT.

exportLastCheckpointOnly boolean

Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false.

evaluationConfig object (EvaluationConfig)

Optional. Evaluation Config for Tuning Job.

tuningMode enum (TuningMode)

Tuning mode.

JSON representation 
{
  "trainingDatasetUri": string,
  "validationDatasetUri": string,
  "hyperParameters": {
    object (SupervisedHyperParameters)
  },
  "exportLastCheckpointOnly": boolean,
  "evaluationConfig": {
    object (EvaluationConfig)
  },
  "tuningMode": enum (TuningMode)
}

SupervisedHyperParameters

Hyperparameters for SFT.

Fields
epochCount string (int64 format)

Optional. Number of complete passes the model makes over the entire training dataset during training.

learningRateMultiplier number

Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with learningRate. This feature is only available for 1P models.

learningRate number

Optional. Learning rate for tuning. Mutually exclusive with learningRateMultiplier. This feature is only available for open source models.

adapterSize enum (AdapterSize)

Optional. Adapter size for tuning.

batchSize string (int64 format)

Optional. Batch size for tuning. This feature is only available for open source models.

JSON representation 
{
  "epochCount": string,
  "learningRateMultiplier": number,
  "learningRate": number,
  "adapterSize": enum (AdapterSize),
  "batchSize": string
}

AdapterSize

Supported adapter sizes for tuning.

Enums
ADAPTER_SIZE_UNSPECIFIED Adapter size is unspecified.
ADAPTER_SIZE_ONE Adapter size 1.
ADAPTER_SIZE_TWO Adapter size 2.
ADAPTER_SIZE_FOUR Adapter size 4.
ADAPTER_SIZE_EIGHT Adapter size 8.
ADAPTER_SIZE_SIXTEEN Adapter size 16.
ADAPTER_SIZE_THIRTY_TWO Adapter size 32.

EvaluationConfig

Evaluation Config for Tuning Job.

Fields
metrics[] object (Metric)

Required. The metrics used for evaluation.

outputConfig object (OutputConfig)

Required. Config for evaluation output.

autoraterConfig object (AutoraterConfig)

Optional. Autorater config for evaluation.

JSON representation 
{
  "metrics": [
    {
      object (Metric)
    }
  ],
  "outputConfig": {
    object (OutputConfig)
  },
  "autoraterConfig": {
    object (AutoraterConfig)
  }
}

Metric

The metric used for running evaluations.

Fields
aggregationMetrics[] enum (AggregationMetric)

Optional. The aggregation metrics to use.

metric_spec Union type
The spec for the metric. It would be either a pre-defined metric, or a inline metric spec. metric_spec can be only one of the following:
pointwiseMetricSpec object (PointwiseMetricSpec)

Spec for pointwise metric.

pairwiseMetricSpec object (PairwiseMetricSpec)

Spec for pairwise metric.

exactMatchSpec object (ExactMatchSpec)

Spec for exact match metric.

bleuSpec object (BleuSpec)

Spec for bleu metric.

rougeSpec object (RougeSpec)

Spec for rouge metric.

JSON representation 
{
  "aggregationMetrics": [
    enum (AggregationMetric)
  ],

  // metric_spec
  "pointwiseMetricSpec": {
    object (PointwiseMetricSpec)
  },
  "pairwiseMetricSpec": {
    object (PairwiseMetricSpec)
  },
  "exactMatchSpec": {
    object (ExactMatchSpec)
  },
  "bleuSpec": {
    object (BleuSpec)
  },
  "rougeSpec": {
    object (RougeSpec)
  }
  // Union type
}

PointwiseMetricSpec

Spec for pointwise metric.

Fields
customOutputFormatConfig object (CustomOutputFormatConfig)

Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the score and explanation fields in the corresponding metric result will be empty.

metricPromptTemplate string

Required. Metric prompt template for pointwise metric.

systemInstruction string

Optional. System instructions for pointwise metric.

JSON representation 
{
  "customOutputFormatConfig": {
    object (CustomOutputFormatConfig)
  },
  "metricPromptTemplate": string,
  "systemInstruction": string
}

CustomOutputFormatConfig

Spec for custom output format configuration.

Fields
custom_output_format_config Union type
Custom output format configuration. custom_output_format_config can be only one of the following:
returnRawOutput boolean

Optional. Whether to return raw output.

JSON representation 
{

  // custom_output_format_config
  "returnRawOutput": boolean
  // Union type
}

PairwiseMetricSpec

Spec for pairwise metric.

Fields
candidateResponseFieldName string

Optional. The field name of the candidate response.

baselineResponseFieldName string

Optional. The field name of the baseline response.

customOutputFormatConfig object (CustomOutputFormatConfig)

Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the pairwiseChoice and explanation fields in the corresponding metric result will be empty.

metricPromptTemplate string

Required. Metric prompt template for pairwise metric.

systemInstruction string

Optional. System instructions for pairwise metric.

JSON representation 
{
  "candidateResponseFieldName": string,
  "baselineResponseFieldName": string,
  "customOutputFormatConfig": {
    object (CustomOutputFormatConfig)
  },
  "metricPromptTemplate": string,
  "systemInstruction": string
}

ExactMatchSpec

This type has no fields.

Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0.

BleuSpec

Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1.

Fields
useEffectiveOrder boolean

Optional. Whether to useEffectiveOrder to compute bleu score.

JSON representation 
{
  "useEffectiveOrder": boolean
}

RougeSpec

Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1.

Fields
rougeType string

Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.

useStemmer boolean

Optional. Whether to use stemmer to compute rouge score.

splitSummaries boolean

Optional. Whether to split summaries while using rougeLsum.

JSON representation 
{
  "rougeType": string,
  "useStemmer": boolean,
  "splitSummaries": boolean
}

AggregationMetric

The aggregation metrics supported by EvaluationService.EvaluateDataset.

Enums
AGGREGATION_METRIC_UNSPECIFIED Unspecified aggregation metric.
AVERAGE Average aggregation metric. Not supported for Pairwise metric.
MODE Mode aggregation metric.
STANDARD_DEVIATION Standard deviation aggregation metric. Not supported for pairwise metric.
VARIANCE Variance aggregation metric. Not supported for pairwise metric.
MINIMUM Minimum aggregation metric. Not supported for pairwise metric.
MAXIMUM Maximum aggregation metric. Not supported for pairwise metric.
MEDIAN Median aggregation metric. Not supported for pairwise metric.
PERCENTILE_P90 90th percentile aggregation metric. Not supported for pairwise metric.
PERCENTILE_P95 95th percentile aggregation metric. Not supported for pairwise metric.
PERCENTILE_P99 99th percentile aggregation metric. Not supported for pairwise metric.

OutputConfig

Config for evaluation output.

Fields
destination Union type
The destination for evaluation output. destination can be only one of the following:
gcsDestination object (GcsDestination)

Cloud storage destination for evaluation output.

JSON representation 
{

  // destination
  "gcsDestination": {
    object (GcsDestination)
  }
  // Union type
}

GcsDestination

The Google Cloud Storage location where the output is to be written to.

Fields
outputUriPrefix string

Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

JSON representation 
{
  "outputUriPrefix": string
}

AutoraterConfig

The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset.

Fields
autoraterModel string

Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use.

Publisher model format: projects/{project}/locations/{location}/publishers/*/models/*

Tuned model endpoint format: projects/{project}/locations/{location}/endpoints/{endpoint}

samplingCount integer

Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.

flipEnabled boolean

Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.

JSON representation 
{
  "autoraterModel": string,
  "samplingCount": integer,
  "flipEnabled": boolean
}

TuningMode

Supported tuning modes.

Enums
TUNING_MODE_UNSPECIFIED Tuning mode is unspecified.
TUNING_MODE_FULL Full fine-tuning mode.
TUNING_MODE_PEFT_ADAPTER PEFT adapter tuning mode.

DistillationSpec

Tuning Spec for Distillation.

Fields
trainingDatasetUri
(deprecated) string

Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

hyperParameters object (DistillationHyperParameters)

Optional. Hyperparameters for Distillation.

studentModel
(deprecated) string

The student model that is being tuned, e.g., "google/gemma-2b-1.1-it". Deprecated. Use baseModel instead.

pipelineRootDirectory
(deprecated) string

Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.

teacher_model Union type
The teacher model that is being distilled from. See Supported models. teacher_model can be only one of the following:
baseTeacherModel string

The base teacher model that is being distilled. See Supported models.

tunedTeacherModelSource string

The resource name of the Tuned teacher model. Format: projects/{project}/locations/{location}/models/{model}.

validationDatasetUri string

Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

JSON representation 
{
  "trainingDatasetUri": string,
  "hyperParameters": {
    object (DistillationHyperParameters)
  },
  "studentModel": string,
  "pipelineRootDirectory": string,

  // teacher_model
  "baseTeacherModel": string,
  "tunedTeacherModelSource": string
  // Union type
  "validationDatasetUri": string
}

DistillationHyperParameters

Hyperparameters for Distillation.

Fields
adapterSize enum (AdapterSize)

Optional. Adapter size for distillation.

epochCount string (int64 format)

Optional. Number of complete passes the model makes over the entire training dataset during training.

learningRateMultiplier number

Optional. Multiplier for adjusting the default learning rate.

JSON representation 
{
  "adapterSize": enum (AdapterSize),
  "epochCount": string,
  "learningRateMultiplier": number
}

PartnerModelTuningSpec

Tuning spec for Partner models.

Fields
trainingDatasetUri string

Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

validationDatasetUri string

Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

hyperParameters map (key: string, value: value (Value format))

Hyperparameters for tuning. The accepted hyperParameters and their valid range of values will differ depending on the base model.

JSON representation 
{
  "trainingDatasetUri": string,
  "validationDatasetUri": string,
  "hyperParameters": {
    string: value,
    ...
  }
}

PreferenceOptimizationSpec

Tuning Spec for Preference Optimization.

Fields
trainingDatasetUri string

Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.

hyperParameters object (PreferenceOptimizationHyperParameters)

Optional. Hyperparameters for Preference Optimization.

evaluationConfig object (EvaluationConfig)

Optional. Evaluation Config for Preference Optimization Job.

validationDatasetUri string

Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.

JSON representation 
{
  "trainingDatasetUri": string,
  "hyperParameters": {
    object (PreferenceOptimizationHyperParameters)
  },
  "evaluationConfig": {
    object (EvaluationConfig)
  },
  "validationDatasetUri": string
}

PreferenceOptimizationHyperParameters

Hyperparameters for Preference Optimization.

Fields
adapterSize enum (AdapterSize)

Optional. Adapter size for preference optimization.

epochCount string (int64 format)

Optional. Number of complete passes the model makes over the entire training dataset during training.

learningRateMultiplier number

Optional. Multiplier for adjusting the default learning rate.

beta number

Optional. weight for KL Divergence regularization.

JSON representation 
{
  "adapterSize": enum (AdapterSize),
  "epochCount": string,
  "learningRateMultiplier": number,
  "beta": number
}

VeoTuningSpec

Tuning Spec for Veo Model Tuning.

Fields
trainingDatasetUri string

Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

validationDatasetUri string

Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

hyperParameters object (VeoHyperParameters)

Optional. Hyperparameters for Veo.

JSON representation 
{
  "trainingDatasetUri": string,
  "validationDatasetUri": string,
  "hyperParameters": {
    object (VeoHyperParameters)
  }
}

VeoHyperParameters

Hyperparameters for Veo.

Fields
epochCount string (int64 format)

Optional. Number of complete passes the model makes over the entire training dataset during training.

learningRateMultiplier number

Optional. Multiplier for adjusting the default learning rate.

tuningTask enum (TuningTask)

Optional. The tuning task. Either I2V or T2V.

JSON representation 
{
  "epochCount": string,
  "learningRateMultiplier": number,
  "tuningTask": enum (TuningTask)
}

TuningTask

An enum defining the tuning task used for Veo.

Enums
TUNING_TASK_UNSPECIFIED Default value. This value is unused.
TUNING_TASK_I2V Tuning task for image to video.
TUNING_TASK_T2V Tuning task for text to video.

JobState

Describes the state of a job.

Enums
JOB_STATE_UNSPECIFIED The job state is unspecified.
JOB_STATE_QUEUED The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING The service is preparing to run the job.
JOB_STATE_RUNNING The job is in progress.
JOB_STATE_SUCCEEDED The job completed successfully.
JOB_STATE_FAILED The job failed.
JOB_STATE_CANCELLING The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED The job has been cancelled.
JOB_STATE_PAUSED The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED The job has expired.
JOB_STATE_UPDATING The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED The job is partially succeeded, some results may be missing due to errors.

TuningJobState

Represents the detailed state of the tuning job while the overall JobState is running.

Enums
TUNING_JOB_STATE_UNSPECIFIED Default tuning job state.
TUNING_JOB_STATE_WAITING_FOR_QUOTA Tuning job is waiting for job quota.
TUNING_JOB_STATE_PROCESSING_DATASET Tuning job is validating the dataset.
TUNING_JOB_STATE_WAITING_FOR_CAPACITY Tuning job is waiting for hardware capacity.
TUNING_JOB_STATE_TUNING Tuning job is running.
TUNING_JOB_STATE_POST_PROCESSING Tuning job is doing some post processing steps.

TunedModel

The Model Registry Model and Online Prediction Endpoint associated with this TuningJob.

Fields
model string

Output only. The resource name of the TunedModel. Format:

projects/{project}/locations/{location}/models/{model}@{versionId}

When tuning from a base model, the versionId will be 1.

For continuous tuning, the version id will be incremented by 1 from the last version id in the parent model. E.g.,

projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}

endpoint string

Output only. A resource name of an Endpoint. Format: projects/{project}/locations/{location}/endpoints/{endpoint}.

checkpoints[] object (TunedModelCheckpoint)

Output only. The checkpoints associated with this TunedModel. This field is only populated for tuning jobs that enable intermediate checkpoints.

JSON representation 
{
  "model": string,
  "endpoint": string,
  "checkpoints": [
    {
      object (TunedModelCheckpoint)
    }
  ]
}

TunedModelCheckpoint

TunedModelCheckpoint for the Tuned Model of a Tuning Job.

Fields
checkpointId string

The id of the checkpoint.

epoch string (int64 format)

The epoch of the checkpoint.

step string (int64 format)

The step of the checkpoint.

endpoint string

The Endpoint resource name that the checkpoint is deployed to. Format: projects/{project}/locations/{location}/endpoints/{endpoint}.

JSON representation 
{
  "checkpointId": string,
  "epoch": string,
  "step": string,
  "endpoint": string
}

TuningDataStats

The tuning data statistic values for TuningJob.

Fields
tuning_data_stats Union type
tuning_data_stats can be only one of the following:
supervisedTuningDataStats object (SupervisedTuningDataStats)

The SFT Tuning data stats.

distillationDataStats object (DistillationDataStats)

Output only. Statistics for distillation.

preferenceOptimizationDataStats object (PreferenceOptimizationDataStats)

Output only. Statistics for preference optimization.

JSON representation 
{

  // tuning_data_stats
  "supervisedTuningDataStats": {
    object (SupervisedTuningDataStats)
  },
  "distillationDataStats": {
    object (DistillationDataStats)
  },
  "preferenceOptimizationDataStats": {
    object (PreferenceOptimizationDataStats)
  }
  // Union type
}

SupervisedTuningDataStats

Tuning data statistics for Supervised Tuning.

Fields
tuningDatasetExampleCount string (int64 format)

Output only. Number of examples in the tuning dataset.

totalTuningCharacterCount string (int64 format)

Output only. Number of tuning characters in the tuning dataset.

totalBillableCharacterCount
(deprecated) string (int64 format)

Output only. Number of billable characters in the tuning dataset.

totalBillableTokenCount string (int64 format)

Output only. Number of billable tokens in the tuning dataset.

tuningStepCount string (int64 format)

Output only. Number of tuning steps for this Tuning Job.

userInputTokenDistribution object (SupervisedTuningDatasetDistribution)

Output only. Dataset distributions for the user input tokens.

userOutputTokenDistribution object (SupervisedTuningDatasetDistribution)

Output only. Dataset distributions for the user output tokens.

userMessagePerExampleDistribution object (SupervisedTuningDatasetDistribution)

Output only. Dataset distributions for the messages per example.

userDatasetExamples[] object (Content)

Output only. Sample user messages in the training dataset uri.

totalTruncatedExampleCount string (int64 format)

Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc.

truncatedExampleIndices[] string (int64 format)

Output only. A partial sample of the indices (starting from 1) of the dropped examples.

droppedExampleReasons[] string

Output only. For each index in truncatedExampleIndices, the user-facing reason why the example was dropped.

JSON representation 
{
  "tuningDatasetExampleCount": string,
  "totalTuningCharacterCount": string,
  "totalBillableCharacterCount": string,
  "totalBillableTokenCount": string,
  "tuningStepCount": string,
  "userInputTokenDistribution": {
    object (SupervisedTuningDatasetDistribution)
  },
  "userOutputTokenDistribution": {
    object (SupervisedTuningDatasetDistribution)
  },
  "userMessagePerExampleDistribution": {
    object (SupervisedTuningDatasetDistribution)
  },
  "userDatasetExamples": [
    {
      object (Content)
    }
  ],
  "totalTruncatedExampleCount": string,
  "truncatedExampleIndices": [
    string
  ],
  "droppedExampleReasons": [
    string
  ]
}

SupervisedTuningDatasetDistribution

Dataset distribution for Supervised Tuning.

Fields
sum string (int64 format)

Output only. Sum of a given population of values.

billableSum string (int64 format)

Output only. Sum of a given population of values that are billable.

min number

Output only. The minimum of the population values.

max number

Output only. The maximum of the population values.

mean number

Output only. The arithmetic mean of the values in the population.

median number

Output only. The median of the values in the population.

p5 number

Output only. The 5th percentile of the values in the population.

p95 number

Output only. The 95th percentile of the values in the population.

buckets[] object (DatasetBucket)

Output only. Defines the histogram bucket.

JSON representation 
{
  "sum": string,
  "billableSum": string,
  "min": number,
  "max": number,
  "mean": number,
  "median": number,
  "p5": number,
  "p95": number,
  "buckets": [
    {
      object (DatasetBucket)
    }
  ]
}

DatasetBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

Fields
count number

Output only. Number of values in the bucket.

left number

Output only. left bound of the bucket.

right number

Output only. Right bound of the bucket.

JSON representation 
{
  "count": number,
  "left": number,
  "right": number
}

DistillationDataStats

Statistics computed for datasets used for distillation.

Fields
trainingDatasetStats object (DatasetStats)

Output only. Statistics computed for the training dataset.

JSON representation 
{
  "trainingDatasetStats": {
    object (DatasetStats)
  }
}

DatasetStats

Statistics computed over a tuning dataset.

Fields
tuningDatasetExampleCount string (int64 format)

Output only. Number of examples in the tuning dataset.

totalTuningCharacterCount string (int64 format)

Output only. Number of tuning characters in the tuning dataset.

totalBillableCharacterCount string (int64 format)

Output only. Number of billable characters in the tuning dataset.

tuningStepCount string (int64 format)

Output only. Number of tuning steps for this Tuning Job.

userInputTokenDistribution object (DatasetDistribution)

Output only. Dataset distributions for the user input tokens.

userMessagePerExampleDistribution object (DatasetDistribution)

Output only. Dataset distributions for the messages per example.

userDatasetExamples[] object (Content)

Output only. Sample user messages in the training dataset uri.

droppedExampleIndices[] string (int64 format)

Output only. A partial sample of the indices (starting from 1) of the dropped examples.

droppedExampleReasons[] string

Output only. For each index in droppedExampleIndices, the user-facing reason why the example was dropped.

userOutputTokenDistribution object (DatasetDistribution)

Output only. Dataset distributions for the user output tokens.

JSON representation 
{
  "tuningDatasetExampleCount": string,
  "totalTuningCharacterCount": string,
  "totalBillableCharacterCount": string,
  "tuningStepCount": string,
  "userInputTokenDistribution": {
    object (DatasetDistribution)
  },
  "userMessagePerExampleDistribution": {
    object (DatasetDistribution)
  },
  "userDatasetExamples": [
    {
      object (Content)
    }
  ],
  "droppedExampleIndices": [
    string
  ],
  "droppedExampleReasons": [
    string
  ],
  "userOutputTokenDistribution": {
    object (DatasetDistribution)
  }
}

DatasetDistribution

Distribution computed over a tuning dataset.

Fields
sum number

Output only. Sum of a given population of values.

min number

Output only. The minimum of the population values.

max number

Output only. The maximum of the population values.

mean number

Output only. The arithmetic mean of the values in the population.

median number

Output only. The median of the values in the population.

p5 number

Output only. The 5th percentile of the values in the population.

p95 number

Output only. The 95th percentile of the values in the population.

buckets[] object (DistributionBucket)

Output only. Defines the histogram bucket.

JSON representation 
{
  "sum": number,
  "min": number,
  "max": number,
  "mean": number,
  "median": number,
  "p5": number,
  "p95": number,
  "buckets": [
    {
      object (DistributionBucket)
    }
  ]
}

DistributionBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

Fields
count string (int64 format)

Output only. Number of values in the bucket.

left number

Output only. left bound of the bucket.

right number

Output only. Right bound of the bucket.

JSON representation 
{
  "count": string,
  "left": number,
  "right": number
}

PreferenceOptimizationDataStats

Statistics computed for datasets used for preference optimization.

Fields
tuningDatasetExampleCount string (int64 format)

Output only. Number of examples in the tuning dataset.

totalBillableTokenCount string (int64 format)

Output only. Number of billable tokens in the tuning dataset.

tuningStepCount string (int64 format)

Output only. Number of tuning steps for this Tuning Job.

userInputTokenDistribution object (DatasetDistribution)

Output only. Dataset distributions for the user input tokens.

userOutputTokenDistribution object (DatasetDistribution)

Output only. Dataset distributions for the user output tokens.

scoreVariancePerExampleDistribution object (DatasetDistribution)

Output only. Dataset distributions for scores variance per example.

scoresDistribution object (DatasetDistribution)

Output only. Dataset distributions for scores.

userDatasetExamples[] object (GeminiPreferenceExample)

Output only. Sample user examples in the training dataset.

droppedExampleIndices[] string (int64 format)

Output only. A partial sample of the indices (starting from 1) of the dropped examples.

droppedExampleReasons[] string

Output only. For each index in droppedExampleIndices, the user-facing reason why the example was dropped.

JSON representation 
{
  "tuningDatasetExampleCount": string,
  "totalBillableTokenCount": string,
  "tuningStepCount": string,
  "userInputTokenDistribution": {
    object (DatasetDistribution)
  },
  "userOutputTokenDistribution": {
    object (DatasetDistribution)
  },
  "scoreVariancePerExampleDistribution": {
    object (DatasetDistribution)
  },
  "scoresDistribution": {
    object (DatasetDistribution)
  },
  "userDatasetExamples": [
    {
      object (GeminiPreferenceExample)
    }
  ],
  "droppedExampleIndices": [
    string
  ],
  "droppedExampleReasons": [
    string
  ]
}

GeminiPreferenceExample

Input example for preference optimization.

Fields
contents[] object (Content)

Multi-turn contents that represents the Prompt.

completions[] object (Completion)

List of completions for a given prompt.

JSON representation 
{
  "contents": [
    {
      object (Content)
    }
  ],
  "completions": [
    {
      object (Completion)
    }
  ]
}

Completion

Completion and its preference score.

Fields
completion object (Content)

Single turn completion for the given prompt.

score number

The score for the given completion.

JSON representation 
{
  "completion": {
    object (Content)
  },
  "score": number
}

EvaluateDatasetRun

Evaluate Dataset Run result for Tuning Job.

Fields
operationName string

Output only. The operation id of the evaluation run. Format: projects/{project}/locations/{location}/operations/{operationId}.

checkpointId string

Output only. The checkpoint id used in the evaluation run. Only populated when evaluating checkpoints.

error object (Status)

Output only. The error of the evaluation run if any.

JSON representation 
{
  "operationName": string,
  "checkpointId": string,
  "error": {
    object (Status)
  }
}

Methods

cancel

 Cancels a TuningJob.

create

 Creates a TuningJob.

get

 Gets a TuningJob.

list

 Lists TuningJobs in a Location.

optimizePrompt

 Optimizes a prompt.

rebaseTunedModel

 Rebase a TunedModel.