oci_generative_ai_model

This resource provides the Model resource in Oracle Cloud Infrastructure Generative Ai service.

Creates a custom model by fine-tuning a base model with your own dataset. You can create a new custom models or create a new version of existing custom model..

The header contains an opc-work-request-id, which is the id for the WorkRequest that tracks the model creation progress.

Example Usage

resource "oci_generative_ai_model" "test_model" {
	#Required
	base_model_id = oci_generative_ai_model.test_model.id
	compartment_id = var.compartment_id
	fine_tune_details {
		#Required
		dedicated_ai_cluster_id = oci_generative_ai_dedicated_ai_cluster.test_dedicated_ai_cluster.id
		training_dataset {
			#Required
			bucket = var.model_fine_tune_details_training_dataset_bucket
			dataset_type = var.model_fine_tune_details_training_dataset_dataset_type
			namespace = var.model_fine_tune_details_training_dataset_namespace
			object = var.model_fine_tune_details_training_dataset_object
		}

		#Optional
		training_config {
			#Required
			training_config_type = var.model_fine_tune_details_training_config_training_config_type

			#Optional
			early_stopping_patience = var.model_fine_tune_details_training_config_early_stopping_patience
			early_stopping_threshold = var.model_fine_tune_details_training_config_early_stopping_threshold
			learning_rate = var.model_fine_tune_details_training_config_learning_rate
			log_model_metrics_interval_in_steps = var.model_fine_tune_details_training_config_log_model_metrics_interval_in_steps
			num_of_last_layers = var.model_fine_tune_details_training_config_num_of_last_layers
			total_training_epochs = var.model_fine_tune_details_training_config_total_training_epochs
			training_batch_size = var.model_fine_tune_details_training_config_training_batch_size
		}
	}

	#Optional
	defined_tags = {"Operations.CostCenter"= "42"}
	description = var.model_description
	display_name = var.model_display_name
	freeform_tags = {"Department"= "Finance"}
	vendor = var.model_vendor
	version = var.model_version
}

Argument Reference

The following arguments are supported:

• base_model_id - (Required) The OCID of the base model that’s used for fine-tuning.

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

• defined_tags - (Optional) (Updatable) 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 - (Optional) (Updatable) An optional description of the model.

• display_name - (Optional) (Updatable) A user-friendly name.

• fine_tune_details - (Required) Details about fine-tuning a custom model.

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

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

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

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

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

    • log_model_metrics_interval_in_steps - (Optional) 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 - (Applicable when training_config_type=VANILLA_TRAINING_CONFIG) The number of last layers to be fine-tuned.

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

    • training_batch_size - (Optional) The batch size used during training.

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

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

• freeform_tags - (Optional) (Updatable) 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"}

• vendor - (Optional) (Updatable) The provider of the model.

• version - (Optional) (Updatable) The version of the model.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

Attributes 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.

Timeouts

The timeouts block allows you to specify timeouts for certain operations: * create - (Defaults to 20 minutes), when creating the Model * update - (Defaults to 20 minutes), when updating the Model * delete - (Defaults to 20 minutes), when destroying the Model

Import

Models can be imported using the id, e.g.

$ terraform import oci_generative_ai_model.test_model "id"