<ahref="https://platform.openai.com/docs/api-reference/chat/create"target="_blank"rel="noopener noreferrer">OpenAI Create chat completion</a>, and let you call **Azure OpenAI, Anthropic, Cohere, Replicate** models in the same format.
In addition, liteLLM allows you to pass in the following **Optional** liteLLM args:<br>
ID of the model to use. See the <ahref="https://litellm.readthedocs.io/en/latest/supported"target="_blank"rel="noopener noreferrer">model endpoint compatibility</a> table for details on which models work with the Chat API.
A list of messages comprising the conversation so far. <ahref="https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"target="_blank"rel="noopener noreferrer">Example Python Code</a>
>> The name of the author of this message. name is required if role is function, and it should be the name of the function whose response is in the content. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
Controls how the model responds to function calls. "none" means the model does not call a function, and responds to the end-user. "auto" means the model can pick between an end-user or calling a function. Specifying a particular function via `{"name": "my_function"}` forces the model to call that function. "none" is the default when no functions are present. "auto" is the default if functions are present.
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.
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 1temperature` but not both.
If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a `data: [DONE]` message.
The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length
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.
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.
<spanstyle="color:gray; font-size: 0.8em;">map</span><spanstyle="color:gray; font-size: 0.8em;">Optional, Defaults to null</span><br>
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 the likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
