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CreateCompletionRequest.md

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OpenapiClient::CreateCompletionRequest

Properties

Name Type Description Notes
model CreateCompletionRequestModel
prompt CreateCompletionRequestPrompt
best_of Integer 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`. [optional][default to 1]
echo Boolean Echo back the prompt in addition to the completion [optional][default to false]
frequency_penalty Float 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. [optional][default to 0]
logit_bias Hash<String, Integer> 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 (which works for both GPT-2 and GPT-3) 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 `{&quot;50256&quot;: -100}` to prevent the < endoftext
logprobs Integer Include the log probabilities on the `logprobs` most likely 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. [optional]
max_tokens Integer The maximum number of tokens to generate 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. [optional][default to 16]
n Integer 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`. [optional][default to 1]
presence_penalty Float 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. [optional][default to 0]
seed Integer 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. [optional]
stop CreateCompletionRequestStop [optional]
stream Boolean Whether to stream back partial progress. If set, 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. [optional][default to false]
suffix String The suffix that comes after a completion of inserted text. [optional]
temperature Float 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 Float 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]
user String A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more. [optional]

Example

require 'openapi_client'

instance = OpenapiClient::CreateCompletionRequest.new(
  model: null,
  prompt: null,
  best_of: null,
  echo: null,
  frequency_penalty: null,
  logit_bias: null,
  logprobs: null,
  max_tokens: 16,
  n: 1,
  presence_penalty: null,
  seed: null,
  stop: null,
  stream: null,
  suffix: test.,
  temperature: 1,
  top_p: 1,
  user: user-1234
)