Oracle Cloud Infrastructure v2.31.0, Apr 17 25

Oracle Cloud Infrastructure v2.31.0 published on Thursday, Apr 17, 2025 by Pulumi

oci.GenerativeAi.getModel

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Oracle Cloud Infrastructure v2.31.0 published on Thursday, Apr 17, 2025 by Pulumi

Example Usage

TypeScript
Python
Go
C#
Java
YAML

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

const testModel = oci.GenerativeAi.getModel({
    modelId: testModelOciGenerativeAiModel.id,
});

import pulumi
import pulumi_oci as oci

test_model = oci.GenerativeAi.get_model(model_id=test_model_oci_generative_ai_model["id"])

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.GetModel(ctx, &generativeai.GetModelArgs{
			ModelId: testModelOciGenerativeAiModel.Id,
		}, 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 testModel = Oci.GenerativeAi.GetModel.Invoke(new()
    {
        ModelId = testModelOciGenerativeAiModel.Id,
    });

});

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.GetModelArgs;
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 testModel = GenerativeAiFunctions.getModel(GetModelArgs.builder()
            .modelId(testModelOciGenerativeAiModel.id())
            .build());

    }
}

variables:
  testModel:
    fn::invoke:
      function: oci:GenerativeAi:getModel
      arguments:
        modelId: ${testModelOciGenerativeAiModel.id}

Using getModel

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.

TypeScript
Python
Go
C#
Java
YAML

function getModel(args: GetModelArgs, opts?: InvokeOptions): Promise<GetModelResult>
function getModelOutput(args: GetModelOutputArgs, opts?: InvokeOptions): Output<GetModelResult>

def get_model(model_id: Optional[str] = None,
              opts: Optional[InvokeOptions] = None) -> GetModelResult
def get_model_output(model_id: Optional[pulumi.Input[str]] = None,
              opts: Optional[InvokeOptions] = None) -> Output[GetModelResult]

func GetModel(ctx *Context, args *GetModelArgs, opts ...InvokeOption) (*GetModelResult, error)
func GetModelOutput(ctx *Context, args *GetModelOutputArgs, opts ...InvokeOption) GetModelResultOutput

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

public static class GetModel 
{
    public static Task<GetModelResult> InvokeAsync(GetModelArgs args, InvokeOptions? opts = null)
    public static Output<GetModelResult> Invoke(GetModelInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetModelResult> getModel(GetModelArgs args, InvokeOptions options)
public static Output<GetModelResult> getModel(GetModelArgs args, InvokeOptions options)

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

The following arguments are supported:

ModelId string: The model OCID

ModelId string: The model OCID

modelId String: The model OCID

modelId string: The model OCID

model_id str: The model OCID

modelId String: The model OCID

getModel Result

The following output properties are available:

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 compartment OCID for fine-tuned models. For pretrained models, this value is null.
DefinedTags Dictionary<string, string>
Description string: An optional description of the model.
DisplayName string: A user-friendly name.
FineTuneDetails List<GetModelFineTuneDetail>: 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: An ID that uniquely identifies a pretrained or fine-tuned 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.
ModelId string
ModelMetrics List<GetModelModelMetric>: 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: The provider of the base model.
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 compartment OCID for fine-tuned models. For pretrained models, this value is null.
DefinedTags map[string]string
Description string: An optional description of the model.
DisplayName string: A user-friendly name.
FineTuneDetails []GetModelFineTuneDetail: 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: An ID that uniquely identifies a pretrained or fine-tuned 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.
ModelId string
ModelMetrics []GetModelModelMetric: 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: The provider of the base model.
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 compartment OCID for fine-tuned models. For pretrained models, this value is null.
definedTags Map<String,String>
description String: An optional description of the model.
displayName String: A user-friendly name.
fineTuneDetails List<GetModelFineTuneDetail>: 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: An ID that uniquely identifies a pretrained or fine-tuned 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.
modelId String
modelMetrics List<GetModelModelMetric>: 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: The provider of the base model.
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 compartment OCID for fine-tuned models. For pretrained models, this value is null.
definedTags {[key: string]: string}
description string: An optional description of the model.
displayName string: A user-friendly name.
fineTuneDetails GetModelFineTuneDetail[]: 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: An ID that uniquely identifies a pretrained or fine-tuned 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.
modelId string
modelMetrics GetModelModelMetric[]: 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: The provider of the base model.
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 compartment OCID for fine-tuned models. For pretrained models, this value is null.
defined_tags Mapping[str, str]
description str: An optional description of the model.
display_name str: A user-friendly name.
fine_tune_details Sequence[generativeai.GetModelFineTuneDetail]: 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: An ID that uniquely identifies a pretrained or fine-tuned 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_id str
model_metrics Sequence[generativeai.GetModelModelMetric]: 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: The provider of the base model.
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 compartment OCID for fine-tuned models. For pretrained models, this value is null.
definedTags Map<String>
description String: An optional description of the model.
displayName String: A user-friendly name.
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: An ID that uniquely identifies a pretrained or fine-tuned 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.
modelId String
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: The provider of the base model.
version String: The version of the model.

Supporting Types

GetModelFineTuneDetail

DedicatedAiClusterId string: The OCID of the dedicated AI cluster this fine-tuning runs on.
TrainingConfigs List<GetModelFineTuneDetailTrainingConfig>: The fine-tuning method and hyperparameters used for fine-tuning a custom model.
TrainingDatasets List<GetModelFineTuneDetailTrainingDataset>: The dataset used to fine-tune the model.

DedicatedAiClusterId string: The OCID of the dedicated AI cluster this fine-tuning runs on.
TrainingConfigs []GetModelFineTuneDetailTrainingConfig: The fine-tuning method and hyperparameters used for fine-tuning a custom model.
TrainingDatasets []GetModelFineTuneDetailTrainingDataset: The dataset used to fine-tune the model.

dedicatedAiClusterId String: The OCID of the dedicated AI cluster this fine-tuning runs on.
trainingConfigs List<GetModelFineTuneDetailTrainingConfig>: The fine-tuning method and hyperparameters used for fine-tuning a custom model.
trainingDatasets List<GetModelFineTuneDetailTrainingDataset>: The dataset used to fine-tune the model.

dedicatedAiClusterId string: The OCID of the dedicated AI cluster this fine-tuning runs on.
trainingConfigs GetModelFineTuneDetailTrainingConfig[]: The fine-tuning method and hyperparameters used for fine-tuning a custom model.
trainingDatasets GetModelFineTuneDetailTrainingDataset[]: 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.GetModelFineTuneDetailTrainingConfig]: The fine-tuning method and hyperparameters used for fine-tuning a custom model.
training_datasets Sequence[generativeai.GetModelFineTuneDetailTrainingDataset]: 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.

GetModelFineTuneDetailTrainingConfig

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.

GetModelFineTuneDetailTrainingDataset

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.

GetModelModelMetric

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 v2.31.0 published on Thursday, Apr 17, 2025 by Pulumi

pulumi/pulumi-oci

oci.GenerativeAi.getModel

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Example Usage

Using getModel

getModel Result

Supporting Types

GetModelFineTuneDetail

GetModelFineTuneDetailTrainingConfig

GetModelFineTuneDetailTrainingDataset

GetModelModelMetric

Package Details

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