import type { MetadataBearer as __MetadataBearer } from "@smithy/types"; import type { GetCustomModelRequest, GetCustomModelResponse } from "../models/models_0"; /** * @public */ export type { __MetadataBearer }; /** * @public * * The input for {@link GetCustomModelCommand}. */ export interface GetCustomModelCommandInput extends GetCustomModelRequest { } /** * @public * * The output of {@link GetCustomModelCommand}. */ export interface GetCustomModelCommandOutput extends GetCustomModelResponse, __MetadataBearer { } declare const GetCustomModelCommand_base: { new (input: GetCustomModelCommandInput): import("@smithy/core/client").CommandImpl; new (input: GetCustomModelCommandInput): import("@smithy/core/client").CommandImpl; getEndpointParameterInstructions(): import("@smithy/types").EndpointParameterInstructions; }; /** *

Get the properties associated with a Amazon Bedrock custom model that you have created. For more information, see Custom models in the Amazon Bedrock User Guide.

* @example * Use a bare-bones client and the command you need to make an API call. * ```javascript * import { BedrockClient, GetCustomModelCommand } from "@aws-sdk/client-bedrock"; // ES Modules import * // const { BedrockClient, GetCustomModelCommand } = require("@aws-sdk/client-bedrock"); // CommonJS import * // import type { BedrockClientConfig } from "@aws-sdk/client-bedrock"; * const config = {}; // type is BedrockClientConfig * const client = new BedrockClient(config); * const input = { // GetCustomModelRequest * modelIdentifier: "STRING_VALUE", // required * }; * const command = new GetCustomModelCommand(input); * const response = await client.send(command); * // { // GetCustomModelResponse * // modelArn: "STRING_VALUE", // required * // modelName: "STRING_VALUE", // required * // jobName: "STRING_VALUE", * // jobArn: "STRING_VALUE", * // baseModelArn: "STRING_VALUE", * // customizationType: "FINE_TUNING" || "CONTINUED_PRE_TRAINING" || "DISTILLATION" || "REINFORCEMENT_FINE_TUNING" || "IMPORTED", * // modelKmsKeyArn: "STRING_VALUE", * // hyperParameters: { // ModelCustomizationHyperParameters * // "": "STRING_VALUE", * // }, * // trainingDataConfig: { // TrainingDataConfig * // s3Uri: "STRING_VALUE", * // invocationLogsConfig: { // InvocationLogsConfig * // usePromptResponse: true || false, * // invocationLogSource: { // InvocationLogSource Union: only one key present * // s3Uri: "STRING_VALUE", * // }, * // requestMetadataFilters: { // RequestMetadataFilters Union: only one key present * // equals: { // RequestMetadataMap * // "": "STRING_VALUE", * // }, * // notEquals: { * // "": "STRING_VALUE", * // }, * // andAll: [ // RequestMetadataFiltersList * // { // RequestMetadataBaseFilters * // equals: { * // "": "STRING_VALUE", * // }, * // notEquals: { * // "": "STRING_VALUE", * // }, * // }, * // ], * // orAll: [ * // { * // equals: { * // "": "STRING_VALUE", * // }, * // notEquals: "", * // }, * // ], * // }, * // }, * // }, * // validationDataConfig: { // ValidationDataConfig * // validators: [ // Validators // required * // { // Validator * // s3Uri: "STRING_VALUE", // required * // }, * // ], * // }, * // outputDataConfig: { // OutputDataConfig * // s3Uri: "STRING_VALUE", // required * // }, * // trainingMetrics: { // TrainingMetrics * // trainingLoss: Number("float"), * // }, * // validationMetrics: [ // ValidationMetrics * // { // ValidatorMetric * // validationLoss: Number("float"), * // }, * // ], * // creationTime: new Date("TIMESTAMP"), // required * // customizationConfig: { // CustomizationConfig Union: only one key present * // distillationConfig: { // DistillationConfig * // teacherModelConfig: { // TeacherModelConfig * // teacherModelIdentifier: "STRING_VALUE", // required * // maxResponseLengthForInference: Number("int"), * // }, * // }, * // rftConfig: { // RFTConfig * // graderConfig: { // GraderConfig Union: only one key present * // lambdaGrader: { // LambdaGraderConfig * // lambdaArn: "STRING_VALUE", // required * // }, * // }, * // hyperParameters: { // RFTHyperParameters * // epochCount: Number("int"), * // batchSize: Number("int"), * // learningRate: Number("float"), * // maxPromptLength: Number("int"), * // trainingSamplePerPrompt: Number("int"), * // inferenceMaxTokens: Number("int"), * // reasoningEffort: "low" || "medium" || "high", * // evalInterval: Number("int"), * // }, * // }, * // }, * // modelStatus: "Active" || "Creating" || "Failed", * // failureMessage: "STRING_VALUE", * // }; * * ``` * * @param GetCustomModelCommandInput - {@link GetCustomModelCommandInput} * @returns {@link GetCustomModelCommandOutput} * @see {@link GetCustomModelCommandInput} for command's `input` shape. * @see {@link GetCustomModelCommandOutput} for command's `response` shape. * @see {@link BedrockClientResolvedConfig | config} for BedrockClient's `config` shape. * * @throws {@link AccessDeniedException} (client fault) *

The request is denied because of missing access permissions.

* * @throws {@link InternalServerException} (server fault) *

An internal server error occurred. Retry your request.

* * @throws {@link ResourceNotFoundException} (client fault) *

The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.

* * @throws {@link ThrottlingException} (client fault) *

The number of requests exceeds the limit. Resubmit your request later.

* * @throws {@link ValidationException} (client fault) *

Input validation failed. Check your request parameters and retry the request.

* * @throws {@link BedrockServiceException} *

Base exception class for all service exceptions from Bedrock service.

* * * @public */ export declare class GetCustomModelCommand extends GetCustomModelCommand_base { /** @internal type navigation helper, not in runtime. */ protected static __types: { api: { input: GetCustomModelRequest; output: GetCustomModelResponse; }; sdk: { input: GetCustomModelCommandInput; output: GetCustomModelCommandOutput; }; }; }