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feat: Integration with Vertex AI Multimodal Datasets: Update train_dataset and validation_dataset in sft.train() docstring to include the Vertex Multimodal Dataset as a dataset source option. #5354

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Jun 10, 2025
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feat: Integration with Vertex AI Multimodal Datasets: Update train_dataset and validation_dataset in sft.train() docstring to include the Vertex Multimodal Dataset as a dataset source option. #5354

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8 changes: 2 additions & 6 deletions google/cloud/aiplatform_v1/types/tuning_job.py
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Expand Up @@ -529,13 +529,9 @@ class SupervisedTuningSpec(proto.Message):

Attributes:
training_dataset_uri (str):
Required. Cloud Storage path to file
containing training dataset for tuning. The
dataset must be formatted as a JSONL file.
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.
validation_dataset_uri (str):
Optional. Cloud Storage path to file
containing validation dataset for tuning. The
dataset must be formatted as a JSONL file.
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.
hyper_parameters (google.cloud.aiplatform_v1.types.SupervisedHyperParameters):
Optional. Hyperparameters for SFT.
export_last_checkpoint_only (bool):
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9 changes: 2 additions & 7 deletions google/cloud/aiplatform_v1beta1/types/tuning_job.py
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Expand Up @@ -764,14 +764,9 @@ class SupervisedTuningSpec(proto.Message):

Attributes:
training_dataset_uri (str):
Required. Cloud Storage path to file
containing training dataset for tuning. The
dataset must be formatted as a JSONL file.
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.
validation_dataset_uri (str):
Optional. Cloud Storage path to file
containing validation dataset for tuning. The
dataset must be formatted as a JSONL file.
hyper_parameters (google.cloud.aiplatform_v1beta1.types.SupervisedHyperParameters):
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.a1.types.SupervisedHyperParameters):
Optional. Hyperparameters for SFT.
export_last_checkpoint_only (bool):
Optional. If set to true, disable
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6 changes: 2 additions & 4 deletions vertexai/tuning/_supervised_tuning.py
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Expand Up @@ -38,10 +38,8 @@ def train(

Args:
source_model (str): Model name for tuning, e.g., "gemini-1.0-pro-002".
train_dataset: Cloud Storage path to file containing training dataset for
tuning. The dataset should be in JSONL format.
validation_dataset: Cloud Storage path to file containing validation
dataset for tuning. The dataset should be in JSONL format.
train_dataset: 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.
validation_dataset: 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.
tuned_model_display_name: The display name of the
[TunedModel][google.cloud.aiplatform.v1.Model]. The name can be up to
128 characters long and can consist of any UTF-8 characters.
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