CreateChatCompletionSettings

dev.maxmelnyk.openaiscala.models.settings.CreateChatCompletionSettings
case class CreateChatCompletionSettings(model: String, temperature: Option[Double], topP: Option[Double], n: Option[Long], stop: Option[Seq[String]], maxTokens: Option[Long], presencePenalty: Option[Double], frequencyPenalty: Option[Double], logitBias: Option[Map[String, Long]], user: Option[String])

Attributes

frequencyPenalty

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

logitBias

Modify the likelihood of specified tokens appearing in the completion. Accepts a Map that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

maxTokens

The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens).

model

ID of the model to use.

n

How many chat completion choices to generate for each input message.

presencePenalty

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

stop

Up to 4 sequences where the API will stop generating further tokens.

temperature

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or topP but not both.

topP

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with topP 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.

user

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.

Graph
Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any

Members list

Concise view

Value members

Inherited methods

def productElementNames: Iterator[String]

Attributes

Inherited from:
Product
def productIterator: Iterator[Any]

Attributes

Inherited from:
Product