NULL.
- `baseModelArn` string — Amazon Resource Name (ARN) of the base model.
- `customizationType` 'FINE_TUNING' | 'CONTINUED_PRE_TRAINING' | 'DISTILLATION' | 'REINFORCEMENT_FINE_TUNING' | 'IMPORTED' — The type of model customization.
- `modelKmsKeyArn` string — The custom model is encrypted at rest using this key.
- `hyperParameters` object — Hyperparameter values associated with this model. For details on the format for different models, see Custom model hyperparameters.
- `trainingDataConfig` object — Contains information about the training dataset.
- `s3Uri` string — The S3 URI where the training data is stored.
- `invocationLogsConfig` object — Settings for using invocation logs to customize a model.
- `usePromptResponse` boolean — Whether to use the model's response for training, or just the prompt. The default value is False.
- `invocationLogSource` object, required — The source of the invocation logs.
- `s3Uri` string — The URI of an invocation log in a bucket.
- `requestMetadataFilters` object — Rules for filtering invocation logs based on request metadata.
- `equals` object — Include results where the key equals the value.
- `notEquals` object — Include results where the key does not equal the value.
- `andAll` RequestMetadataBaseFilters[] — Include results where all of the based filters match.
- `equals` object — Include results where the key equals the value.
- `notEquals` object — Include results where the key does not equal the value.
- `orAll` RequestMetadataBaseFilters[] — Include results where any of the base filters match.
- `equals` object — Include results where the key equals the value.
- `notEquals` object — Include results where the key does not equal the value.
- `validationDataConfig` object — Contains information about the validation dataset.
- `validators` Validator[], required — Information about the validators.
- `s3Uri` string, required — The S3 URI where the validation data is stored.
- `outputDataConfig` object — Output data configuration associated with this custom model.
- `s3Uri` string, required — The S3 URI where the output data is stored.
- `trainingMetrics` object — Contains training metrics from the job creation.
- `trainingLoss` number, float — Loss metric associated with the custom job.
- `validationMetrics` ValidatorMetric[] — The validation metrics from the job creation.
- `validationLoss` number, float — The validation loss associated with this validator.
- `creationTime` string, date-time, required — Creation time of the model.
- `customizationConfig` object — The customization configuration for the custom model.
- `distillationConfig` object — The Distillation configuration for the custom model.
- `teacherModelConfig` object, required — The teacher model configuration.
- `teacherModelIdentifier` string, required — The identifier of the teacher model.
- `maxResponseLengthForInference` integer — The maximum number of tokens requested when the customization job invokes the teacher model.
- `rftConfig` object — Configuration settings for reinforcement fine-tuning (RFT) model customization, including grader configuration and hyperparameters.
- `graderConfig` object — Configuration for the grader that evaluates model responses and provides reward signals during RFT training.
- `lambdaGrader` object — Configuration for using an AWS Lambda function as the grader for evaluating model responses and provide reward signals in reinforcement fine-tuning.
- `lambdaArn` string, required — ARN of the AWS Lambda function that will evaluate model responses and return reward scores for RFT training.
- `hyperParameters` object — Hyperparameters that control the reinforcement fine-tuning training process, including learning rate, batch size, and epoch count.
- `epochCount` integer — Number of training epochs to run during reinforcement fine-tuning. Higher values may improve performance but increase training time.
- `batchSize` integer — Number of training samples processed in each batch during reinforcement fine-tuning (RFT) training. Larger batches may improve training stability.
- `learningRate` number, float — Learning rate for the reinforcement fine-tuning. Controls how quickly the model adapts to reward signals.
- `maxPromptLength` integer — Maximum length of input prompts during RFT training, measured in tokens. Longer prompts allow more context but increase memory usage and training-time.
- `trainingSamplePerPrompt` integer — Number of response samples generated per prompt during RFT training. More samples provide better reward signal estimation.
- `inferenceMaxTokens` integer — Maximum number of tokens the model can generate in response to each prompt during RFT training.
- `reasoningEffort` 'low' | 'medium' | 'high' — Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.
- `evalInterval` integer — Interval between evaluation runs during RFT training, measured in training steps. More frequent evaluation provides better monitoring.
- `modelStatus` 'Active' | 'Creating' | 'Failed' — The current status of the custom model. Possible values include:
Creating - The model is being created and validated.
Active - The model has been successfully created and is ready for use.
Failed - The model creation process failed. Check the failureMessage field for details.