Data Source: oci_generative_ai_models

This data source provides the list of Models in Oracle Cloud Infrastructure Generative Ai service.

Lists the models in a specific compartment. Includes pretrained base models and fine-tuned custom models.

Example Usage

data "oci_generative_ai_models" "test_models" {
	#Required
	compartment_id = var.compartment_id

	#Optional
	capability = var.model_capability
	display_name = var.model_display_name
	id = var.model_id
	state = var.model_state
	vendor = var.model_vendor
}

Argument Reference

The following arguments are supported:

• capability - (Optional) A filter to return only resources their capability matches the given capability.
• compartment_id - (Required) The OCID of the compartment in which to list resources.
• display_name - (Optional) A filter to return only resources that match the given display name exactly.
• id - (Optional) The ID of the model.
• state - (Optional) A filter to return only resources their lifecycleState matches the given lifecycleState.
• vendor - (Optional) A filter to return only resources that match the entire vendor given.

Attributes Reference

The following attributes are exported:

• model_collection - The list of model_collection.

Model Reference

The following attributes are exported:

• base_model_id - The OCID of the base model that’s used for fine-tuning. For pretrained models, the value is null.

• capabilities - Describes what this model can be used for.

• compartment_id - The compartment OCID for fine-tuned models. For pretrained models, this value is null.

• defined_tags - Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {"Operations.CostCenter": "42"}

• description - An optional description of the model.

• display_name - A user-friendly name.

• fine_tune_details - Details about fine-tuning a custom model.

  • dedicated_ai_cluster_id - The OCID of the dedicated AI cluster this fine-tuning runs on.

  • training_config - The fine-tuning method and hyperparameters used for fine-tuning a custom model.

    • early_stopping_patience - Stop training if the loss metric does not improve beyond ‘early_stopping_threshold’ for this many times of evaluation.

    • early_stopping_threshold - How much the loss must improve to prevent early stopping.

    • learning_rate - The initial learning rate to be used during training

    • log_model_metrics_interval_in_steps - Determines how frequently to log model metrics.

      Every step is logged for the first 20 steps and then follows this parameter for log frequency. Set to 0 to disable logging the model metrics.

    • num_of_last_layers - The number of last layers to be fine-tuned.

    • total_training_epochs - The maximum number of training epochs to run for.

    • training_batch_size - The batch size used during training.

    • training_config_type - The fine-tuning method for training a custom model.

  • training_dataset - The dataset used to fine-tune the model.

    Only one dataset is allowed per custom model, which is split 90-10 for training and validating. You must provide the dataset in a JSON Lines (JSONL) file. Each line in the JSONL file must have the format: {"prompt": "<first prompt>", "completion": "<expected completion given first prompt>"}

    • bucket - The Object Storage bucket name.
    • dataset_type - The type of the data asset.
    • namespace - The Object Storage namespace.
    • object - The Object Storage object name.

• freeform_tags - Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {"Department": "Finance"}

• id - An ID that uniquely identifies a pretrained or fine-tuned model.

• is_long_term_supported - Whether a model is supported long-term. Only applicable to base models.

• lifecycle_details - A message describing the current state of the model in more detail that can provide actionable information.

• model_metrics - Model metrics during the creation of a new model.

  • final_accuracy - Fine-tuned model accuracy.
  • final_loss - Fine-tuned model loss.
  • model_metrics_type - The type of the model metrics. Each type of model can expect a different set of model metrics.

• state - The lifecycle state of the model.

• system_tags - System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}

• time_created - The date and time that the model was created in the format of an RFC3339 datetime string.

• time_deprecated - Corresponds to the time when the custom model and its associated foundation model will be deprecated.

• time_updated - The date and time that the model was updated in the format of an RFC3339 datetime string.

• type - The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.

• vendor - The provider of the base model.

• version - The version of the model.