Oracle Cloud Infrastructure v2.17.0, Nov 15 24

Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi

oci.GenerativeAi.getModels

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Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi

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

Items List<GetModelsModelCollectionItem>

Items []GetModelsModelCollectionItem

items List<GetModelsModelCollectionItem>

items GetModelsModelCollectionItem[]

items Sequence[generativeai.GetModelsModelCollectionItem]

items List<Property Map>

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.

Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi

pulumi/pulumi-oci

oci.GenerativeAi.getModels

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Example Usage

Using getModels

getModels Result

Supporting Types

GetModelsFilter

GetModelsModelCollection

GetModelsModelCollectionItem

GetModelsModelCollectionItemFineTuneDetail

GetModelsModelCollectionItemFineTuneDetailTrainingConfig

GetModelsModelCollectionItemFineTuneDetailTrainingDataset

GetModelsModelCollectionItemModelMetric

Package Details

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