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.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 traininglog_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.
lora_alpha
- This parameter represents the scaling factor for the weight matrices in LoRA.lora_dropout
- This parameter indicates the dropout probability for LoRA layers.lora_r
- This parameter represents the LoRA rank of the update matrices.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 80-20 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.vendor
- The provider of the base model.version
- The version of the model.