# CreateChatCompletionRequest

## Properties

Name | Type | Description | Notes
------------ | ------------- | ------------- | -------------
**model** | **String** | ID of the model to use. Currently, only `gpt-3.5-turbo` and `gpt-3.5-turbo-0301` are supported. | 
**messages** | [**Vec<crate::models::ChatCompletionRequestMessage>**](ChatCompletionRequestMessage.md) | The messages to generate chat completions for, in the [chat format](/docs/guides/chat/introduction). | 
**temperature** | Option<**f32**> | 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 `top_p` but not both.  | [optional][default to 1]
**top_p** | Option<**f32**> | 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.  | [optional][default to 1]
**n** | Option<**i32**> | How many chat completion choices to generate for each input message. | [optional][default to 1]
**stream** | Option<**bool**> | If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message.  | [optional][default to false]
**stop** | Option<[**crate::models::CreateChatCompletionRequestStop**](CreateChatCompletionRequest_stop.md)> |  | [optional]
**max_tokens** | Option<**i32**> | 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).  | [optional]
**presence_penalty** | Option<**f32**> | 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.  [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)  | [optional][default to 0]
**frequency_penalty** | Option<**f32**> | 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.  [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)  | [optional][default to 0]
**logit_bias** | Option<[**serde_json::Value**](.md)> | Modify the likelihood of specified tokens appearing in the completion.  Accepts a json object 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.  | [optional]
**user** | Option<**String**> | A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).  | [optional]

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