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Oracle Cloud Infrastructure v2.11.0 published on Thursday, Sep 19, 2024 by Pulumi
pulumi/pulumi-oci

oci.GenerativeAi.getModels

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Oracle Cloud Infrastructure v2.11.0 published on Thursday, Sep 19, 2024 by Pulumi
pulumi/pulumi-oci

    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

    import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";

const testModels = oci.GenerativeAi.getModels({
    compartmentId: compartmentId,
    capabilities: modelCapability,
    displayName: modelDisplayName,
    id: modelId,
    state: modelState,
    vendor: modelVendor,
});

    import pulumi
import pulumi_oci as oci

test_models = oci.GenerativeAi.get_models(compartment_id=compartment_id,
    capabilities=model_capability,
    display_name=model_display_name,
    id=model_id,
    state=model_state,
    vendor=model_vendor)

    package main

import (
	"github.com/pulumi/pulumi-oci/sdk/v2/go/oci/GenerativeAi"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := GenerativeAi.GetModels(ctx, &generativeai.GetModelsArgs{
			CompartmentId: compartmentId,
			Capabilities:  modelCapability,
			DisplayName:   pulumi.StringRef(modelDisplayName),
			Id:            pulumi.StringRef(modelId),
			State:         pulumi.StringRef(modelState),
			Vendor:        pulumi.StringRef(modelVendor),
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Oci = Pulumi.Oci;

return await Deployment.RunAsync(() => 
{
    var testModels = Oci.GenerativeAi.GetModels.Invoke(new()
    {
        CompartmentId = compartmentId,
        Capabilities = modelCapability,
        DisplayName = modelDisplayName,
        Id = modelId,
        State = modelState,
        Vendor = modelVendor,
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.GenerativeAi.GenerativeAiFunctions;
import com.pulumi.oci.GenerativeAi.inputs.GetModelsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var testModels = GenerativeAiFunctions.getModels(GetModelsArgs.builder()
            .compartmentId(compartmentId)
            .capabilities(modelCapability)
            .displayName(modelDisplayName)
            .id(modelId)
            .state(modelState)
            .vendor(modelVendor)
            .build());

    }
}

    variables:
  testModels:
    fn::invoke:
      Function: oci:GenerativeAi:getModels
      Arguments:
        compartmentId: ${compartmentId}
        capabilities: ${modelCapability}
        displayName: ${modelDisplayName}
        id: ${modelId}
        state: ${modelState}
        vendor: ${modelVendor}

    Using getModels

    Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

    function getModels(args: GetModelsArgs, opts?: InvokeOptions): Promise<GetModelsResult>
function getModelsOutput(args: GetModelsOutputArgs, opts?: InvokeOptions): Output<GetModelsResult>
    def get_models(capabilities: Optional[Sequence[str]] = None,
               compartment_id: Optional[str] = None,
               display_name: Optional[str] = None,
               filters: Optional[Sequence[_generativeai.GetModelsFilter]] = None,
               id: Optional[str] = None,
               state: Optional[str] = None,
               vendor: Optional[str] = None,
               opts: Optional[InvokeOptions] = None) -> GetModelsResult
def get_models_output(capabilities: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
               compartment_id: Optional[pulumi.Input[str]] = None,
               display_name: Optional[pulumi.Input[str]] = None,
               filters: Optional[pulumi.Input[Sequence[pulumi.Input[_generativeai.GetModelsFilterArgs]]]] = None,
               id: Optional[pulumi.Input[str]] = None,
               state: Optional[pulumi.Input[str]] = None,
               vendor: Optional[pulumi.Input[str]] = None,
               opts: Optional[InvokeOptions] = None) -> Output[GetModelsResult]
    func GetModels(ctx *Context, args *GetModelsArgs, opts ...InvokeOption) (*GetModelsResult, error)
func GetModelsOutput(ctx *Context, args *GetModelsOutputArgs, opts ...InvokeOption) GetModelsResultOutput

    > Note: This function is named GetModels in the Go SDK.

    public static class GetModels 
{
    public static Task<GetModelsResult> InvokeAsync(GetModelsArgs args, InvokeOptions? opts = null)
    public static Output<GetModelsResult> Invoke(GetModelsInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

    fn::invoke:
  function: oci:GenerativeAi/getModels:getModels
  arguments:
    # arguments dictionary

    The following arguments are supported:

    CompartmentId string
    The OCID of the compartment in which to list resources.
    Capabilities List<string>
    A filter to return only resources their capability matches the given capability.
    DisplayName string
    A filter to return only resources that match the given display name exactly.
    Filters List<GetModelsFilter>
    Id string
    The ID of the model.
    State string
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    Vendor string
    A filter to return only resources that match the entire vendor given.
    CompartmentId string
    The OCID of the compartment in which to list resources.
    Capabilities []string
    A filter to return only resources their capability matches the given capability.
    DisplayName string
    A filter to return only resources that match the given display name exactly.
    Filters []GetModelsFilter
    Id string
    The ID of the model.
    State string
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    Vendor string
    A filter to return only resources that match the entire vendor given.
    compartmentId String
    The OCID of the compartment in which to list resources.
    capabilities List<String>
    A filter to return only resources their capability matches the given capability.
    displayName String
    A filter to return only resources that match the given display name exactly.
    filters List<GetModelsFilter>
    id String
    The ID of the model.
    state String
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    vendor String
    A filter to return only resources that match the entire vendor given.
    compartmentId string
    The OCID of the compartment in which to list resources.
    capabilities string[]
    A filter to return only resources their capability matches the given capability.
    displayName string
    A filter to return only resources that match the given display name exactly.
    filters GetModelsFilter[]
    id string
    The ID of the model.
    state string
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    vendor string
    A filter to return only resources that match the entire vendor given.
    compartment_id str
    The OCID of the compartment in which to list resources.
    capabilities Sequence[str]
    A filter to return only resources their capability matches the given capability.
    display_name str
    A filter to return only resources that match the given display name exactly.
    filters Sequence[generativeai.GetModelsFilter]
    id str
    The ID of the model.
    state str
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    vendor str
    A filter to return only resources that match the entire vendor given.
    compartmentId String
    The OCID of the compartment in which to list resources.
    capabilities List<String>
    A filter to return only resources their capability matches the given capability.
    displayName String
    A filter to return only resources that match the given display name exactly.
    filters List<Property Map>
    id String
    The ID of the model.
    state String
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    vendor String
    A filter to return only resources that match the entire vendor given.

    getModels Result

    The following output properties are available:

    CompartmentId string
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    ModelCollections List<GetModelsModelCollection>
    The list of model_collection.
    Capabilities List<string>
    DisplayName string
    A user-friendly name.
    Filters List<GetModelsFilter>
    Id string
    An ID that uniquely identifies a pretrained or fine-tuned model.
    State string
    The lifecycle state of the model.
    Vendor string
    The provider of the base model.
    CompartmentId string
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    ModelCollections []GetModelsModelCollection
    The list of model_collection.
    Capabilities []string
    DisplayName string
    A user-friendly name.
    Filters []GetModelsFilter
    Id string
    An ID that uniquely identifies a pretrained or fine-tuned model.
    State string
    The lifecycle state of the model.
    Vendor string
    The provider of the base model.
    compartmentId String
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    modelCollections List<GetModelsModelCollection>
    The list of model_collection.
    capabilities List<String>
    displayName String
    A user-friendly name.
    filters List<GetModelsFilter>
    id String
    An ID that uniquely identifies a pretrained or fine-tuned model.
    state String
    The lifecycle state of the model.
    vendor String
    The provider of the base model.
    compartmentId string
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    modelCollections GetModelsModelCollection[]
    The list of model_collection.
    capabilities string[]
    displayName string
    A user-friendly name.
    filters GetModelsFilter[]
    id string
    An ID that uniquely identifies a pretrained or fine-tuned model.
    state string
    The lifecycle state of the model.
    vendor string
    The provider of the base model.
    compartment_id str
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    model_collections Sequence[generativeai.GetModelsModelCollection]
    The list of model_collection.
    capabilities Sequence[str]
    display_name str
    A user-friendly name.
    filters Sequence[generativeai.GetModelsFilter]
    id str
    An ID that uniquely identifies a pretrained or fine-tuned model.
    state str
    The lifecycle state of the model.
    vendor str
    The provider of the base model.
    compartmentId String
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    modelCollections List<Property Map>
    The list of model_collection.
    capabilities List<String>
    displayName String
    A user-friendly name.
    filters List<Property Map>
    id String
    An ID that uniquely identifies a pretrained or fine-tuned model.
    state String
    The lifecycle state of the model.
    vendor String
    The provider of the base model.

    Supporting Types

    GetModelsFilter

    Name string
    Values List<string>
    Regex bool
    Name string
    Values []string
    Regex bool
    name String
    values List<String>
    regex Boolean
    name string
    values string[]
    regex boolean
    name str
    values Sequence[str]
    regex bool
    name String
    values List<String>
    regex Boolean

    GetModelsModelCollection

    GetModelsModelCollectionItem

    BaseModelId string
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    Capabilities List<string>
    Describes what this model can be used for.
    CompartmentId string
    The OCID of the compartment in which to list resources.
    DefinedTags Dictionary<string, string>
    Description string
    An optional description of the model.
    DisplayName string
    A filter to return only resources that match the given display name exactly.
    FineTuneDetails List<GetModelsModelCollectionItemFineTuneDetail>
    Details about fine-tuning a custom model.
    FreeformTags Dictionary<string, string>
    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 string
    The ID of the model.
    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<GetModelsModelCollectionItemModelMetric>
    Model metrics during the creation of a new model.
    State string
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    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
    A filter to return only resources that match the entire vendor given.
    Version string
    The version of the model.
    BaseModelId string
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    Capabilities []string
    Describes what this model can be used for.
    CompartmentId string
    The OCID of the compartment in which to list resources.
    DefinedTags map[string]string
    Description string
    An optional description of the model.
    DisplayName string
    A filter to return only resources that match the given display name exactly.
    FineTuneDetails []GetModelsModelCollectionItemFineTuneDetail
    Details about fine-tuning a custom model.
    FreeformTags map[string]string
    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 string
    The ID of the model.
    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 []GetModelsModelCollectionItemModelMetric
    Model metrics during the creation of a new model.
    State string
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    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
    A filter to return only resources that match the entire vendor given.
    Version string
    The version of the model.
    baseModelId String
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities List<String>
    Describes what this model can be used for.
    compartmentId String
    The OCID of the compartment in which to list resources.
    definedTags Map<String,String>
    description String
    An optional description of the model.
    displayName String
    A filter to return only resources that match the given display name exactly.
    fineTuneDetails List<GetModelsModelCollectionItemFineTuneDetail>
    Details about fine-tuning a custom model.
    freeformTags Map<String,String>
    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 String
    The ID of the model.
    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<GetModelsModelCollectionItemModelMetric>
    Model metrics during the creation of a new model.
    state String
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    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
    A filter to return only resources that match the entire vendor given.
    version String
    The version of the model.
    baseModelId string
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities string[]
    Describes what this model can be used for.
    compartmentId string
    The OCID of the compartment in which to list resources.
    definedTags {[key: string]: string}
    description string
    An optional description of the model.
    displayName string
    A filter to return only resources that match the given display name exactly.
    fineTuneDetails GetModelsModelCollectionItemFineTuneDetail[]
    Details about fine-tuning a custom model.
    freeformTags {[key: string]: string}
    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 string
    The ID of the model.
    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 GetModelsModelCollectionItemModelMetric[]
    Model metrics during the creation of a new model.
    state string
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    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
    A filter to return only resources that match the entire vendor given.
    version string
    The version of the model.
    base_model_id str
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities Sequence[str]
    Describes what this model can be used for.
    compartment_id str
    The OCID of the compartment in which to list resources.
    defined_tags Mapping[str, str]
    description str
    An optional description of the model.
    display_name str
    A filter to return only resources that match the given display name exactly.
    fine_tune_details Sequence[generativeai.GetModelsModelCollectionItemFineTuneDetail]
    Details about fine-tuning a custom model.
    freeform_tags Mapping[str, str]
    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 str
    The ID of the model.
    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.GetModelsModelCollectionItemModelMetric]
    Model metrics during the creation of a new model.
    state str
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    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
    A filter to return only resources that match the entire vendor given.
    version str
    The version of the model.
    baseModelId String
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities List<String>
    Describes what this model can be used for.
    compartmentId String
    The OCID of the compartment in which to list resources.
    definedTags Map<String>
    description String
    An optional description of the model.
    displayName String
    A filter to return only resources that match the given display name exactly.
    fineTuneDetails List<Property Map>
    Details about fine-tuning a custom model.
    freeformTags Map<String>
    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 String
    The ID of the model.
    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
    A filter to return only resources their lifecycleState matches the given lifecycleState.
    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
    A filter to return only resources that match the entire vendor given.
    version String
    The version of the model.

    GetModelsModelCollectionItemFineTuneDetail

    DedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    TrainingConfigs List<GetModelsModelCollectionItemFineTuneDetailTrainingConfig>
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    TrainingDatasets List<GetModelsModelCollectionItemFineTuneDetailTrainingDataset>
    The dataset used to fine-tune the model.
    DedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    TrainingConfigs []GetModelsModelCollectionItemFineTuneDetailTrainingConfig
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    TrainingDatasets []GetModelsModelCollectionItemFineTuneDetailTrainingDataset
    The dataset used to fine-tune the model.
    dedicatedAiClusterId String
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingConfigs List<GetModelsModelCollectionItemFineTuneDetailTrainingConfig>
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    trainingDatasets List<GetModelsModelCollectionItemFineTuneDetailTrainingDataset>
    The dataset used to fine-tune the model.
    dedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingConfigs GetModelsModelCollectionItemFineTuneDetailTrainingConfig[]
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    trainingDatasets GetModelsModelCollectionItemFineTuneDetailTrainingDataset[]
    The dataset used to fine-tune the model.
    dedicated_ai_cluster_id str
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    training_configs Sequence[generativeai.GetModelsModelCollectionItemFineTuneDetailTrainingConfig]
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    training_datasets Sequence[generativeai.GetModelsModelCollectionItemFineTuneDetailTrainingDataset]
    The dataset used to fine-tune the model.
    dedicatedAiClusterId String
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingConfigs List<Property Map>
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    trainingDatasets List<Property Map>
    The dataset used to fine-tune the model.

    GetModelsModelCollectionItemFineTuneDetailTrainingConfig

    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.
    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.
    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.
    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.
    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.
    trainingConfigType string
    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.
    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.
    training_config_type str
    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.
    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.
    trainingConfigType String
    The fine-tuning method for training a custom model.

    GetModelsModelCollectionItemFineTuneDetailTrainingDataset

    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.

    GetModelsModelCollectionItemModelMetric

    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.

    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 v2.11.0 published on Thursday, Sep 19, 2024 by Pulumi
    pulumi/pulumi-oci