DistillationSpec

@Serializable
data class DistillationSpec(val promptDatasetUri: String? = null, val baseTeacherModel: String? = null, val hyperParameters: DistillationHyperParameters? = null, val pipelineRootDirectory: String? = null, val studentModel: String? = null, val trainingDatasetUri: String? = null, val tunedTeacherModelSource: String? = null, val validationDatasetUri: String? = null, val tuningMode: TuningMode? = null)

Distillation tuning spec for tuning.

Constructors

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constructor(promptDatasetUri: String? = null, baseTeacherModel: String? = null, hyperParameters: DistillationHyperParameters? = null, pipelineRootDirectory: String? = null, studentModel: String? = null, trainingDatasetUri: String? = null, tunedTeacherModelSource: String? = null, validationDatasetUri: String? = null, tuningMode: TuningMode? = null)

Properties

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

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Optional. Hyperparameters for Distillation.

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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.

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

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val studentModel: String? = null

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

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

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

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val tuningMode: TuningMode? = null

Optional. Specifies the tuning mode for distillation (sft part). This feature is only available for open source models.

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