Package software.amazon.awssdk.services.sagemaker.model

Interface TextGenerationJobConfig.Builder

    • Method Detail

      • completionCriteria

        TextGenerationJobConfig.Builder completionCriteria​(AutoMLJobCompletionCriteria completionCriteria)

        How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

        Parameters:
        completionCriteria - How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • baseModelName

        TextGenerationJobConfig.Builder baseModelName​(String baseModelName)

        The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.

        Parameters:
        baseModelName - The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • textGenerationHyperParameters

        TextGenerationJobConfig.Builder textGenerationHyperParameters​(Map<String,​String> textGenerationHyperParameters)

        The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

        • "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

        • "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

        • "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

        • "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

        Here is an example where all four hyperparameters are configured.

        { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

        Parameters:
        textGenerationHyperParameters - The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.

        • "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

        • "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

        • "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

        • "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

        Here is an example where all four hyperparameters are configured.

        { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • modelAccessConfig

        TextGenerationJobConfig.Builder modelAccessConfig​(ModelAccessConfig modelAccessConfig)
        Sets the value of the ModelAccessConfig property for this object.
        Parameters:
        modelAccessConfig - The new value for the ModelAccessConfig property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.