final case class CreateEditRequest(model: String, input: Optional[String] = Optional.Absent, instruction: String, n: Optional[CreateEditRequest.N] = Optional.Absent, temperature: Optional[Temperature] = Optional.Absent, topP: Optional[TopP] = Optional.Absent) extends Product with Serializable

CreateEditRequest model

model

ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.

input

The input text to use as a starting point for the edit.

instruction

The instruction that tells the model how to edit the prompt.

n

How many edits to generate for the input and instruction.

temperature

What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both.

topP

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.

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Instance Constructors

  1. new CreateEditRequest(model: String, input: Optional[String] = Optional.Absent, instruction: String, n: Optional[CreateEditRequest.N] = Optional.Absent, temperature: Optional[Temperature] = Optional.Absent, topP: Optional[TopP] = Optional.Absent)

    model

    ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.

    input

    The input text to use as a starting point for the edit.

    instruction

    The instruction that tells the model how to edit the prompt.

    n

    How many edits to generate for the input and instruction.

    temperature

    What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both.

    topP

    An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.

Value Members

  1. final def !=(arg0: Any): Boolean
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  9. val input: Optional[String]
  10. val instruction: String
  11. final def isInstanceOf[T0]: Boolean
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  12. val model: String
  13. val n: Optional[CreateEditRequest.N]
  14. final def ne(arg0: AnyRef): Boolean
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  17. def productElementNames: Iterator[String]
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  18. final def synchronized[T0](arg0: => T0): T0
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  19. val temperature: Optional[Temperature]
  20. val topP: Optional[TopP]
  21. final def wait(): Unit
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