Interface ModelSpeculativeDecodingConfig.Builder
- All Superinterfaces:
Buildable,CopyableBuilder<ModelSpeculativeDecodingConfig.Builder,,ModelSpeculativeDecodingConfig> SdkBuilder<ModelSpeculativeDecodingConfig.Builder,,ModelSpeculativeDecodingConfig> SdkPojo
- Enclosing class:
ModelSpeculativeDecodingConfig
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Method Summary
Modifier and TypeMethodDescriptionThe speculative decoding technique to apply during model optimization.technique(ModelSpeculativeDecodingTechnique technique) The speculative decoding technique to apply during model optimization.trainingDataSource(Consumer<ModelSpeculativeDecodingTrainingDataSource.Builder> trainingDataSource) The location of the training data to use for speculative decoding.trainingDataSource(ModelSpeculativeDecodingTrainingDataSource trainingDataSource) The location of the training data to use for speculative decoding.Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copyMethods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, buildMethods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFieldNameToField, sdkFields
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Method Details
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technique
The speculative decoding technique to apply during model optimization.
- Parameters:
technique- The speculative decoding technique to apply during model optimization.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
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technique
The speculative decoding technique to apply during model optimization.
- Parameters:
technique- The speculative decoding technique to apply during model optimization.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
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trainingDataSource
ModelSpeculativeDecodingConfig.Builder trainingDataSource(ModelSpeculativeDecodingTrainingDataSource trainingDataSource) The location of the training data to use for speculative decoding. The data must be formatted as ShareGPT, OpenAI Completions or OpenAI Chat Completions. The input can also be unencrypted captured data from a SageMaker endpoint as long as the endpoint uses one of the above formats.
- Parameters:
trainingDataSource- The location of the training data to use for speculative decoding. The data must be formatted as ShareGPT, OpenAI Completions or OpenAI Chat Completions. The input can also be unencrypted captured data from a SageMaker endpoint as long as the endpoint uses one of the above formats.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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trainingDataSource
default ModelSpeculativeDecodingConfig.Builder trainingDataSource(Consumer<ModelSpeculativeDecodingTrainingDataSource.Builder> trainingDataSource) The location of the training data to use for speculative decoding. The data must be formatted as ShareGPT, OpenAI Completions or OpenAI Chat Completions. The input can also be unencrypted captured data from a SageMaker endpoint as long as the endpoint uses one of the above formats.
This is a convenience method that creates an instance of theModelSpeculativeDecodingTrainingDataSource.Builderavoiding the need to create one manually viaModelSpeculativeDecodingTrainingDataSource.builder().When the
Consumercompletes,SdkBuilder.build()is called immediately and its result is passed totrainingDataSource(ModelSpeculativeDecodingTrainingDataSource).- Parameters:
trainingDataSource- a consumer that will call methods onModelSpeculativeDecodingTrainingDataSource.Builder- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
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