Interface RFTHyperParameters.Builder
- All Superinterfaces:
Buildable,CopyableBuilder<RFTHyperParameters.Builder,,RFTHyperParameters> SdkBuilder<RFTHyperParameters.Builder,,RFTHyperParameters> SdkPojo
- Enclosing class:
RFTHyperParameters
-
Method Summary
Modifier and TypeMethodDescriptionNumber of training samples processed in each batch during reinforcement fine-tuning (RFT) training.epochCount(Integer epochCount) Number of training epochs to run during reinforcement fine-tuning.evalInterval(Integer evalInterval) Interval between evaluation runs during RFT training, measured in training steps.inferenceMaxTokens(Integer inferenceMaxTokens) Maximum number of tokens the model can generate in response to each prompt during RFT training.learningRate(Float learningRate) Learning rate for the reinforcement fine-tuning.maxPromptLength(Integer maxPromptLength) Maximum length of input prompts during RFT training, measured in tokens.reasoningEffort(String reasoningEffort) Level of reasoning effort applied during RFT training.reasoningEffort(ReasoningEffort reasoningEffort) Level of reasoning effort applied during RFT training.trainingSamplePerPrompt(Integer trainingSamplePerPrompt) Number of response samples generated per prompt during RFT training.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
-
Method Details
-
epochCount
Number of training epochs to run during reinforcement fine-tuning. Higher values may improve performance but increase training time.
- Parameters:
epochCount- Number of training epochs to run during reinforcement fine-tuning. Higher values may improve performance but increase training time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
batchSize
Number of training samples processed in each batch during reinforcement fine-tuning (RFT) training. Larger batches may improve training stability.
- Parameters:
batchSize- Number of training samples processed in each batch during reinforcement fine-tuning (RFT) training. Larger batches may improve training stability.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
learningRate
Learning rate for the reinforcement fine-tuning. Controls how quickly the model adapts to reward signals.
- Parameters:
learningRate- Learning rate for the reinforcement fine-tuning. Controls how quickly the model adapts to reward signals.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
maxPromptLength
Maximum length of input prompts during RFT training, measured in tokens. Longer prompts allow more context but increase memory usage and training-time.
- Parameters:
maxPromptLength- Maximum length of input prompts during RFT training, measured in tokens. Longer prompts allow more context but increase memory usage and training-time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
trainingSamplePerPrompt
Number of response samples generated per prompt during RFT training. More samples provide better reward signal estimation.
- Parameters:
trainingSamplePerPrompt- Number of response samples generated per prompt during RFT training. More samples provide better reward signal estimation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
inferenceMaxTokens
Maximum number of tokens the model can generate in response to each prompt during RFT training.
- Parameters:
inferenceMaxTokens- Maximum number of tokens the model can generate in response to each prompt during RFT training.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
reasoningEffort
Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.
- Parameters:
reasoningEffort- Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
reasoningEffort
Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.
- Parameters:
reasoningEffort- Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
evalInterval
Interval between evaluation runs during RFT training, measured in training steps. More frequent evaluation provides better monitoring.
- Parameters:
evalInterval- Interval between evaluation runs during RFT training, measured in training steps. More frequent evaluation provides better monitoring.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-