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Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi
pulumi/pulumi-oci

oci.GenerativeAi.Model

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Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi
pulumi/pulumi-oci

    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.

    Create Model Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new Model(name: string, args: ModelArgs, opts?: CustomResourceOptions);
    @overload
def Model(resource_name: str,
          args: ModelArgs,
          opts: Optional[ResourceOptions] = None)

@overload
def Model(resource_name: str,
          opts: Optional[ResourceOptions] = None,
          base_model_id: Optional[str] = None,
          compartment_id: Optional[str] = None,
          fine_tune_details: Optional[_generativeai.ModelFineTuneDetailsArgs] = None,
          defined_tags: Optional[Mapping[str, str]] = None,
          description: Optional[str] = None,
          display_name: Optional[str] = None,
          freeform_tags: Optional[Mapping[str, str]] = None,
          vendor: Optional[str] = None,
          version: Optional[str] = None)
    func NewModel(ctx *Context, name string, args ModelArgs, opts ...ResourceOption) (*Model, error)
    public Model(string name, ModelArgs args, CustomResourceOptions? opts = null)
    public Model(String name, ModelArgs args)
public Model(String name, ModelArgs args, CustomResourceOptions options)

    type: oci:GenerativeAi:Model
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var examplemodelResourceResourceFromGenerativeAimodel = new Oci.GenerativeAi.Model("examplemodelResourceResourceFromGenerativeAimodel", new()
{
    BaseModelId = "string",
    CompartmentId = "string",
    FineTuneDetails = new Oci.GenerativeAi.Inputs.ModelFineTuneDetailsArgs
    {
        DedicatedAiClusterId = "string",
        TrainingDataset = new Oci.GenerativeAi.Inputs.ModelFineTuneDetailsTrainingDatasetArgs
        {
            Bucket = "string",
            DatasetType = "string",
            Namespace = "string",
            Object = "string",
        },
        TrainingConfig = new Oci.GenerativeAi.Inputs.ModelFineTuneDetailsTrainingConfigArgs
        {
            TrainingConfigType = "string",
            EarlyStoppingPatience = 0,
            EarlyStoppingThreshold = 0,
            LearningRate = 0,
            LogModelMetricsIntervalInSteps = 0,
            LoraAlpha = 0,
            LoraDropout = 0,
            LoraR = 0,
            NumOfLastLayers = 0,
            TotalTrainingEpochs = 0,
            TrainingBatchSize = 0,
        },
    },
    DefinedTags = 
    {
        { "string", "string" },
    },
    Description = "string",
    DisplayName = "string",
    FreeformTags = 
    {
        { "string", "string" },
    },
    Vendor = "string",
    Version = "string",
});

    example, err := GenerativeAi.NewModel(ctx, "examplemodelResourceResourceFromGenerativeAimodel", &GenerativeAi.ModelArgs{
	BaseModelId:   pulumi.String("string"),
	CompartmentId: pulumi.String("string"),
	FineTuneDetails: &generativeai.ModelFineTuneDetailsArgs{
		DedicatedAiClusterId: pulumi.String("string"),
		TrainingDataset: &generativeai.ModelFineTuneDetailsTrainingDatasetArgs{
			Bucket:      pulumi.String("string"),
			DatasetType: pulumi.String("string"),
			Namespace:   pulumi.String("string"),
			Object:      pulumi.String("string"),
		},
		TrainingConfig: &generativeai.ModelFineTuneDetailsTrainingConfigArgs{
			TrainingConfigType:             pulumi.String("string"),
			EarlyStoppingPatience:          pulumi.Int(0),
			EarlyStoppingThreshold:         pulumi.Float64(0),
			LearningRate:                   pulumi.Float64(0),
			LogModelMetricsIntervalInSteps: pulumi.Int(0),
			LoraAlpha:                      pulumi.Int(0),
			LoraDropout:                    pulumi.Float64(0),
			LoraR:                          pulumi.Int(0),
			NumOfLastLayers:                pulumi.Int(0),
			TotalTrainingEpochs:            pulumi.Int(0),
			TrainingBatchSize:              pulumi.Int(0),
		},
	},
	DefinedTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Description: pulumi.String("string"),
	DisplayName: pulumi.String("string"),
	FreeformTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Vendor:  pulumi.String("string"),
	Version: pulumi.String("string"),
})

    var examplemodelResourceResourceFromGenerativeAimodel = new Model("examplemodelResourceResourceFromGenerativeAimodel", ModelArgs.builder()
    .baseModelId("string")
    .compartmentId("string")
    .fineTuneDetails(ModelFineTuneDetailsArgs.builder()
        .dedicatedAiClusterId("string")
        .trainingDataset(ModelFineTuneDetailsTrainingDatasetArgs.builder()
            .bucket("string")
            .datasetType("string")
            .namespace("string")
            .object("string")
            .build())
        .trainingConfig(ModelFineTuneDetailsTrainingConfigArgs.builder()
            .trainingConfigType("string")
            .earlyStoppingPatience(0)
            .earlyStoppingThreshold(0)
            .learningRate(0)
            .logModelMetricsIntervalInSteps(0)
            .loraAlpha(0)
            .loraDropout(0)
            .loraR(0)
            .numOfLastLayers(0)
            .totalTrainingEpochs(0)
            .trainingBatchSize(0)
            .build())
        .build())
    .definedTags(Map.of("string", "string"))
    .description("string")
    .displayName("string")
    .freeformTags(Map.of("string", "string"))
    .vendor("string")
    .version("string")
    .build());

    examplemodel_resource_resource_from_generative_aimodel = oci.generative_ai.Model("examplemodelResourceResourceFromGenerativeAimodel",
    base_model_id="string",
    compartment_id="string",
    fine_tune_details={
        "dedicated_ai_cluster_id": "string",
        "training_dataset": {
            "bucket": "string",
            "dataset_type": "string",
            "namespace": "string",
            "object": "string",
        },
        "training_config": {
            "training_config_type": "string",
            "early_stopping_patience": 0,
            "early_stopping_threshold": 0,
            "learning_rate": 0,
            "log_model_metrics_interval_in_steps": 0,
            "lora_alpha": 0,
            "lora_dropout": 0,
            "lora_r": 0,
            "num_of_last_layers": 0,
            "total_training_epochs": 0,
            "training_batch_size": 0,
        },
    },
    defined_tags={
        "string": "string",
    },
    description="string",
    display_name="string",
    freeform_tags={
        "string": "string",
    },
    vendor="string",
    version="string")

    const examplemodelResourceResourceFromGenerativeAimodel = new oci.generativeai.Model("examplemodelResourceResourceFromGenerativeAimodel", {
    baseModelId: "string",
    compartmentId: "string",
    fineTuneDetails: {
        dedicatedAiClusterId: "string",
        trainingDataset: {
            bucket: "string",
            datasetType: "string",
            namespace: "string",
            object: "string",
        },
        trainingConfig: {
            trainingConfigType: "string",
            earlyStoppingPatience: 0,
            earlyStoppingThreshold: 0,
            learningRate: 0,
            logModelMetricsIntervalInSteps: 0,
            loraAlpha: 0,
            loraDropout: 0,
            loraR: 0,
            numOfLastLayers: 0,
            totalTrainingEpochs: 0,
            trainingBatchSize: 0,
        },
    },
    definedTags: {
        string: "string",
    },
    description: "string",
    displayName: "string",
    freeformTags: {
        string: "string",
    },
    vendor: "string",
    version: "string",
});

    type: oci:GenerativeAi:Model
properties:
    baseModelId: string
    compartmentId: string
    definedTags:
        string: string
    description: string
    displayName: string
    fineTuneDetails:
        dedicatedAiClusterId: string
        trainingConfig:
            earlyStoppingPatience: 0
            earlyStoppingThreshold: 0
            learningRate: 0
            logModelMetricsIntervalInSteps: 0
            loraAlpha: 0
            loraDropout: 0
            loraR: 0
            numOfLastLayers: 0
            totalTrainingEpochs: 0
            trainingBatchSize: 0
            trainingConfigType: string
        trainingDataset:
            bucket: string
            datasetType: string
            namespace: string
            object: string
    freeformTags:
        string: string
    vendor: string
    version: string

    Model Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The Model resource accepts the following input properties:

    BaseModelId string
    The OCID of the base model that's used for fine-tuning.
    CompartmentId string
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    FineTuneDetails ModelFineTuneDetails
    Details about fine-tuning a custom model.
    DefinedTags Dictionary<string, string>
    (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 string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A user-friendly name.
    FreeformTags Dictionary<string, string>
    (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 string
    (Updatable) The provider of the model.
    Version string

    (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

    BaseModelId string
    The OCID of the base model that's used for fine-tuning.
    CompartmentId string
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    FineTuneDetails ModelFineTuneDetailsArgs
    Details about fine-tuning a custom model.
    DefinedTags map[string]string
    (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 string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A user-friendly name.
    FreeformTags map[string]string
    (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 string
    (Updatable) The provider of the model.
    Version string

    (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

    baseModelId String
    The OCID of the base model that's used for fine-tuning.
    compartmentId String
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    fineTuneDetails ModelFineTuneDetails
    Details about fine-tuning a custom model.
    definedTags Map<String,String>
    (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 String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A user-friendly name.
    freeformTags Map<String,String>
    (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 String
    (Updatable) The provider of the model.
    version String

    (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

    baseModelId string
    The OCID of the base model that's used for fine-tuning.
    compartmentId string
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    fineTuneDetails ModelFineTuneDetails
    Details about fine-tuning a custom model.
    definedTags {[key: string]: string}
    (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 string
    (Updatable) An optional description of the model.
    displayName string
    (Updatable) A user-friendly name.
    freeformTags {[key: string]: string}
    (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 string
    (Updatable) The provider of the model.
    version string

    (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

    base_model_id str
    The OCID of the base model that's used for fine-tuning.
    compartment_id str
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    fine_tune_details generativeai.ModelFineTuneDetailsArgs
    Details about fine-tuning a custom model.
    defined_tags Mapping[str, str]
    (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 str
    (Updatable) An optional description of the model.
    display_name str
    (Updatable) A user-friendly name.
    freeform_tags Mapping[str, str]
    (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 str
    (Updatable) The provider of the model.
    version str

    (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

    baseModelId String
    The OCID of the base model that's used for fine-tuning.
    compartmentId String
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    fineTuneDetails Property Map
    Details about fine-tuning a custom model.
    definedTags Map<String>
    (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 String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A user-friendly name.
    freeformTags Map<String>
    (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 String
    (Updatable) The provider of the model.
    version String

    (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

    Outputs

    All input properties are implicitly available as output properties. Additionally, the Model resource produces the following output properties:

    Capabilities List<string>
    Describes what this model can be used for.
    Id string
    The provider-assigned unique ID for this managed resource.
    IsLongTermSupported bool
    Whether a model is supported long-term. Only applicable to base models.
    LifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    ModelMetrics List<ModelModelMetric>
    Model metrics during the creation of a new model.
    State string
    The lifecycle state of the model.
    SystemTags Dictionary<string, string>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    TimeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    TimeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    Type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    Capabilities []string
    Describes what this model can be used for.
    Id string
    The provider-assigned unique ID for this managed resource.
    IsLongTermSupported bool
    Whether a model is supported long-term. Only applicable to base models.
    LifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    ModelMetrics []ModelModelMetric
    Model metrics during the creation of a new model.
    State string
    The lifecycle state of the model.
    SystemTags map[string]string
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    TimeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    TimeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    Type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    capabilities List<String>
    Describes what this model can be used for.
    id String
    The provider-assigned unique ID for this managed resource.
    isLongTermSupported Boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails String
    A message describing the current state of the model in more detail that can provide actionable information.
    modelMetrics List<ModelModelMetric>
    Model metrics during the creation of a new model.
    state String
    The lifecycle state of the model.
    systemTags Map<String,String>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated String
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated String
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type String
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    capabilities string[]
    Describes what this model can be used for.
    id string
    The provider-assigned unique ID for this managed resource.
    isLongTermSupported boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    modelMetrics ModelModelMetric[]
    Model metrics during the creation of a new model.
    state string
    The lifecycle state of the model.
    systemTags {[key: string]: string}
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    capabilities Sequence[str]
    Describes what this model can be used for.
    id str
    The provider-assigned unique ID for this managed resource.
    is_long_term_supported bool
    Whether a model is supported long-term. Only applicable to base models.
    lifecycle_details str
    A message describing the current state of the model in more detail that can provide actionable information.
    model_metrics Sequence[generativeai.ModelModelMetric]
    Model metrics during the creation of a new model.
    state str
    The lifecycle state of the model.
    system_tags Mapping[str, str]
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    time_created str
    The date and time that the model was created in the format of an RFC3339 datetime string.
    time_deprecated str
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    time_updated str
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type str
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    capabilities List<String>
    Describes what this model can be used for.
    id String
    The provider-assigned unique ID for this managed resource.
    isLongTermSupported Boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails String
    A message describing the current state of the model in more detail that can provide actionable information.
    modelMetrics List<Property Map>
    Model metrics during the creation of a new model.
    state String
    The lifecycle state of the model.
    systemTags Map<String>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated String
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated String
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type String
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.

    Look up Existing Model Resource

    Get an existing Model resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

    public static get(name: string, id: Input<ID>, state?: ModelState, opts?: CustomResourceOptions): Model
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        base_model_id: Optional[str] = None,
        capabilities: Optional[Sequence[str]] = None,
        compartment_id: Optional[str] = None,
        defined_tags: Optional[Mapping[str, str]] = None,
        description: Optional[str] = None,
        display_name: Optional[str] = None,
        fine_tune_details: Optional[_generativeai.ModelFineTuneDetailsArgs] = None,
        freeform_tags: Optional[Mapping[str, str]] = None,
        is_long_term_supported: Optional[bool] = None,
        lifecycle_details: Optional[str] = None,
        model_metrics: Optional[Sequence[_generativeai.ModelModelMetricArgs]] = None,
        state: Optional[str] = None,
        system_tags: Optional[Mapping[str, str]] = None,
        time_created: Optional[str] = None,
        time_deprecated: Optional[str] = None,
        time_updated: Optional[str] = None,
        type: Optional[str] = None,
        vendor: Optional[str] = None,
        version: Optional[str] = None) -> Model
    func GetModel(ctx *Context, name string, id IDInput, state *ModelState, opts ...ResourceOption) (*Model, error)
    public static Model Get(string name, Input<string> id, ModelState? state, CustomResourceOptions? opts = null)
    public static Model get(String name, Output<String> id, ModelState state, CustomResourceOptions options)
    Resource lookup is not supported in YAML
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    resource_name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    The following state arguments are supported:
    BaseModelId string
    The OCID of the base model that's used for fine-tuning.
    Capabilities List<string>
    Describes what this model can be used for.
    CompartmentId string
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    DefinedTags Dictionary<string, string>
    (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 string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A user-friendly name.
    FineTuneDetails ModelFineTuneDetails
    Details about fine-tuning a custom model.
    FreeformTags Dictionary<string, string>
    (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"}
    IsLongTermSupported bool
    Whether a model is supported long-term. Only applicable to base models.
    LifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    ModelMetrics List<ModelModelMetric>
    Model metrics during the creation of a new model.
    State string
    The lifecycle state of the model.
    SystemTags Dictionary<string, string>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    TimeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    TimeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    Type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    Vendor string
    (Updatable) The provider of the model.
    Version string

    (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

    BaseModelId string
    The OCID of the base model that's used for fine-tuning.
    Capabilities []string
    Describes what this model can be used for.
    CompartmentId string
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    DefinedTags map[string]string
    (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 string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A user-friendly name.
    FineTuneDetails ModelFineTuneDetailsArgs
    Details about fine-tuning a custom model.
    FreeformTags map[string]string
    (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"}
    IsLongTermSupported bool
    Whether a model is supported long-term. Only applicable to base models.
    LifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    ModelMetrics []ModelModelMetricArgs
    Model metrics during the creation of a new model.
    State string
    The lifecycle state of the model.
    SystemTags map[string]string
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    TimeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    TimeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    Type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    Vendor string
    (Updatable) The provider of the model.
    Version string

    (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

    baseModelId String
    The OCID of the base model that's used for fine-tuning.
    capabilities List<String>
    Describes what this model can be used for.
    compartmentId String
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    definedTags Map<String,String>
    (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 String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A user-friendly name.
    fineTuneDetails ModelFineTuneDetails
    Details about fine-tuning a custom model.
    freeformTags Map<String,String>
    (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"}
    isLongTermSupported Boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails String
    A message describing the current state of the model in more detail that can provide actionable information.
    modelMetrics List<ModelModelMetric>
    Model metrics during the creation of a new model.
    state String
    The lifecycle state of the model.
    systemTags Map<String,String>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated String
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated String
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type String
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor String
    (Updatable) The provider of the model.
    version String

    (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

    baseModelId string
    The OCID of the base model that's used for fine-tuning.
    capabilities string[]
    Describes what this model can be used for.
    compartmentId string
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    definedTags {[key: string]: string}
    (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 string
    (Updatable) An optional description of the model.
    displayName string
    (Updatable) A user-friendly name.
    fineTuneDetails ModelFineTuneDetails
    Details about fine-tuning a custom model.
    freeformTags {[key: string]: string}
    (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"}
    isLongTermSupported boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    modelMetrics ModelModelMetric[]
    Model metrics during the creation of a new model.
    state string
    The lifecycle state of the model.
    systemTags {[key: string]: string}
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor string
    (Updatable) The provider of the model.
    version string

    (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

    base_model_id str
    The OCID of the base model that's used for fine-tuning.
    capabilities Sequence[str]
    Describes what this model can be used for.
    compartment_id str
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    defined_tags Mapping[str, str]
    (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 str
    (Updatable) An optional description of the model.
    display_name str
    (Updatable) A user-friendly name.
    fine_tune_details generativeai.ModelFineTuneDetailsArgs
    Details about fine-tuning a custom model.
    freeform_tags Mapping[str, str]
    (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"}
    is_long_term_supported bool
    Whether a model is supported long-term. Only applicable to base models.
    lifecycle_details str
    A message describing the current state of the model in more detail that can provide actionable information.
    model_metrics Sequence[generativeai.ModelModelMetricArgs]
    Model metrics during the creation of a new model.
    state str
    The lifecycle state of the model.
    system_tags Mapping[str, str]
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    time_created str
    The date and time that the model was created in the format of an RFC3339 datetime string.
    time_deprecated str
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    time_updated str
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type str
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor str
    (Updatable) The provider of the model.
    version str

    (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

    baseModelId String
    The OCID of the base model that's used for fine-tuning.
    capabilities List<String>
    Describes what this model can be used for.
    compartmentId String
    (Updatable) The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    definedTags Map<String>
    (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 String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A user-friendly name.
    fineTuneDetails Property Map
    Details about fine-tuning a custom model.
    freeformTags Map<String>
    (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"}
    isLongTermSupported Boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails String
    A message describing the current state of the model in more detail that can provide actionable information.
    modelMetrics List<Property Map>
    Model metrics during the creation of a new model.
    state String
    The lifecycle state of the model.
    systemTags Map<String>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated String
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated String
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type String
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor String
    (Updatable) The provider of the model.
    version String

    (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

    Supporting Types

    ModelFineTuneDetails, ModelFineTuneDetailsArgs

    DedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    TrainingDataset ModelFineTuneDetailsTrainingDataset

    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>"}

    TrainingConfig ModelFineTuneDetailsTrainingConfig
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    DedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    TrainingDataset ModelFineTuneDetailsTrainingDataset

    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>"}

    TrainingConfig ModelFineTuneDetailsTrainingConfig
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    dedicatedAiClusterId String
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingDataset ModelFineTuneDetailsTrainingDataset

    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>"}

    trainingConfig ModelFineTuneDetailsTrainingConfig
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    dedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingDataset ModelFineTuneDetailsTrainingDataset

    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>"}

    trainingConfig ModelFineTuneDetailsTrainingConfig
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    dedicated_ai_cluster_id str
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    training_dataset generativeai.ModelFineTuneDetailsTrainingDataset

    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>"}

    training_config generativeai.ModelFineTuneDetailsTrainingConfig
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    dedicatedAiClusterId String
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingDataset Property Map

    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>"}

    trainingConfig Property Map
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.

    ModelFineTuneDetailsTrainingConfig, ModelFineTuneDetailsTrainingConfigArgs

    TrainingConfigType string
    The fine-tuning method for training a custom model.
    EarlyStoppingPatience int
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    EarlyStoppingThreshold double
    How much the loss must improve to prevent early stopping.
    LearningRate double
    The initial learning rate to be used during training
    LogModelMetricsIntervalInSteps int

    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.

    LoraAlpha int
    This parameter represents the scaling factor for the weight matrices in LoRA.
    LoraDropout double
    This parameter indicates the dropout probability for LoRA layers.
    LoraR int
    This parameter represents the LoRA rank of the update matrices.
    NumOfLastLayers int
    The number of last layers to be fine-tuned.
    TotalTrainingEpochs int
    The maximum number of training epochs to run for.
    TrainingBatchSize int
    The batch size used during training.
    TrainingConfigType string
    The fine-tuning method for training a custom model.
    EarlyStoppingPatience int
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    EarlyStoppingThreshold float64
    How much the loss must improve to prevent early stopping.
    LearningRate float64
    The initial learning rate to be used during training
    LogModelMetricsIntervalInSteps int

    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.

    LoraAlpha int
    This parameter represents the scaling factor for the weight matrices in LoRA.
    LoraDropout float64
    This parameter indicates the dropout probability for LoRA layers.
    LoraR int
    This parameter represents the LoRA rank of the update matrices.
    NumOfLastLayers int
    The number of last layers to be fine-tuned.
    TotalTrainingEpochs int
    The maximum number of training epochs to run for.
    TrainingBatchSize int
    The batch size used during training.
    trainingConfigType String
    The fine-tuning method for training a custom model.
    earlyStoppingPatience Integer
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    earlyStoppingThreshold Double
    How much the loss must improve to prevent early stopping.
    learningRate Double
    The initial learning rate to be used during training
    logModelMetricsIntervalInSteps Integer

    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.

    loraAlpha Integer
    This parameter represents the scaling factor for the weight matrices in LoRA.
    loraDropout Double
    This parameter indicates the dropout probability for LoRA layers.
    loraR Integer
    This parameter represents the LoRA rank of the update matrices.
    numOfLastLayers Integer
    The number of last layers to be fine-tuned.
    totalTrainingEpochs Integer
    The maximum number of training epochs to run for.
    trainingBatchSize Integer
    The batch size used during training.
    trainingConfigType string
    The fine-tuning method for training a custom model.
    earlyStoppingPatience number
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    earlyStoppingThreshold number
    How much the loss must improve to prevent early stopping.
    learningRate number
    The initial learning rate to be used during training
    logModelMetricsIntervalInSteps number

    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.

    loraAlpha number
    This parameter represents the scaling factor for the weight matrices in LoRA.
    loraDropout number
    This parameter indicates the dropout probability for LoRA layers.
    loraR number
    This parameter represents the LoRA rank of the update matrices.
    numOfLastLayers number
    The number of last layers to be fine-tuned.
    totalTrainingEpochs number
    The maximum number of training epochs to run for.
    trainingBatchSize number
    The batch size used during training.
    training_config_type str
    The fine-tuning method for training a custom model.
    early_stopping_patience int
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    early_stopping_threshold float
    How much the loss must improve to prevent early stopping.
    learning_rate float
    The initial learning rate to be used during training
    log_model_metrics_interval_in_steps int

    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 int
    This parameter represents the scaling factor for the weight matrices in LoRA.
    lora_dropout float
    This parameter indicates the dropout probability for LoRA layers.
    lora_r int
    This parameter represents the LoRA rank of the update matrices.
    num_of_last_layers int
    The number of last layers to be fine-tuned.
    total_training_epochs int
    The maximum number of training epochs to run for.
    training_batch_size int
    The batch size used during training.
    trainingConfigType String
    The fine-tuning method for training a custom model.
    earlyStoppingPatience Number
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    earlyStoppingThreshold Number
    How much the loss must improve to prevent early stopping.
    learningRate Number
    The initial learning rate to be used during training
    logModelMetricsIntervalInSteps Number

    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.

    loraAlpha Number
    This parameter represents the scaling factor for the weight matrices in LoRA.
    loraDropout Number
    This parameter indicates the dropout probability for LoRA layers.
    loraR Number
    This parameter represents the LoRA rank of the update matrices.
    numOfLastLayers Number
    The number of last layers to be fine-tuned.
    totalTrainingEpochs Number
    The maximum number of training epochs to run for.
    trainingBatchSize Number
    The batch size used during training.

    ModelFineTuneDetailsTrainingDataset, ModelFineTuneDetailsTrainingDatasetArgs

    Bucket string
    The Object Storage bucket name.
    DatasetType string
    The type of the data asset.
    Namespace string
    The Object Storage namespace.
    Object string
    The Object Storage object name.
    Bucket string
    The Object Storage bucket name.
    DatasetType string
    The type of the data asset.
    Namespace string
    The Object Storage namespace.
    Object string
    The Object Storage object name.
    bucket String
    The Object Storage bucket name.
    datasetType String
    The type of the data asset.
    namespace String
    The Object Storage namespace.
    object String
    The Object Storage object name.
    bucket string
    The Object Storage bucket name.
    datasetType string
    The type of the data asset.
    namespace string
    The Object Storage namespace.
    object string
    The Object Storage object name.
    bucket str
    The Object Storage bucket name.
    dataset_type str
    The type of the data asset.
    namespace str
    The Object Storage namespace.
    object str
    The Object Storage object name.
    bucket String
    The Object Storage bucket name.
    datasetType String
    The type of the data asset.
    namespace String
    The Object Storage namespace.
    object String
    The Object Storage object name.

    ModelModelMetric, ModelModelMetricArgs

    FinalAccuracy double
    Fine-tuned model accuracy.
    FinalLoss double
    Fine-tuned model loss.
    ModelMetricsType string
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    FinalAccuracy float64
    Fine-tuned model accuracy.
    FinalLoss float64
    Fine-tuned model loss.
    ModelMetricsType string
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    finalAccuracy Double
    Fine-tuned model accuracy.
    finalLoss Double
    Fine-tuned model loss.
    modelMetricsType String
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    finalAccuracy number
    Fine-tuned model accuracy.
    finalLoss number
    Fine-tuned model loss.
    modelMetricsType string
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    final_accuracy float
    Fine-tuned model accuracy.
    final_loss float
    Fine-tuned model loss.
    model_metrics_type str
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    finalAccuracy Number
    Fine-tuned model accuracy.
    finalLoss Number
    Fine-tuned model loss.
    modelMetricsType String
    The type of the model metrics. Each type of model can expect a different set of model metrics.

    Import

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

    $ pulumi import oci:GenerativeAi/model:Model test_model "id"

    To learn more about importing existing cloud resources, see Importing resources.

    Package Details

    Repository
    oci pulumi/pulumi-oci
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the oci Terraform Provider.
    oci logo
    Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi
    pulumi/pulumi-oci