v1
latestOpenAPI 3.1.02026-07-1766222595.7 KBCompletion
Request body
Generates best_of completions server-side and returns the "best" (the one with the highest log probability per token). Results cannot be streamed.
When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n.
Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.
Controls the number of top tokens to consider. -1 means consider all tokens.
Float that represents the minimum probability for a token to be considered, relative to the probability of the most likely token. Must be in [0, 1]. Set to 0 to disable this.
Whether to use beam search for sampling.
Float that penalizes sequences based on their length. Used in beam search.
Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens.
Controls the stopping condition for beam search. It accepts the following values: True, where the generation stops as soon as there are best_of complete candidates; False, where an heuristic is applied and the generation stops when is it very unlikely to find better candidates; "never", where the beam search procedure only stops when there cannot be better candidates (canonical beam search algorithm).
List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens.
Whether to include the stop strings in output text.
Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.
Minimum number of tokens to generate per output sequence before EOS or stop_token_ids can be generated
Whether to skip special tokens in the output. Only supported in vllm.
Whether to add spaces between special tokens in the output. Only supported in vllm.
If true (the default), special tokens (e.g. BOS) will be added to the prompt.
JSON schema for guided decoding. Only supported in vllm.
Regex for guided decoding. Only supported in vllm.
Choices for guided decoding. Only supported in vllm.
Context-free grammar for guided decoding. Only supported in vllm.
If specified, will override the default guided decoding backend of the server for this specific request. If set, must be either 'outlines' / 'lm-format-enforcer'
If specified, will override the default whitespace pattern for guided json decoding.
ID of the model to use.
Echo back the prompt in addition to the completion
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.
Modify the likelihood of specified tokens appearing in the completion.
Accepts a JSON object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool to convert text to token IDs. 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.
As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.
Include the log probabilities on the logprobs most likely output tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response.
The maximum value for logprobs is 5.
The maximum number of tokens that can be generated in the completion.
The token count of your prompt plus max_tokens cannot exceed the model's context length. Example Python code for counting tokens.
How many completions to generate for each prompt.
Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.
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.
If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.
If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Example Python code.
The suffix that comes after a completion of inserted text.
This parameter is only supported for gpt-3.5-turbo-instruct.
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 temperature but not both.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example request
{
"model": "mixtral-8x7b-instruct",
"max_tokens": 16,
"n": 1,
"suffix": "test.",
"temperature": 1,
"top_p": 1,
"user": "user-1234"
}Response
Successful Response