update

Author: mrbullwinkle

articles/ai-services/openai/includes/api-versions/latest-inference-preview.md

@@ -40,9 +40,9 @@ Creates a completion for the provided prompt, parameters and chosen model.
 | best_of | integer | Generates `best_of` completions server-side and returns the "best" (the one with the highest log probability per token). Results can't be streamed.<br><br>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`.<br><br>**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`.<br> | No | 1 |
 | echo | boolean | Echo back the prompt in addition to the completion<br> | No | False |
 | frequency_penalty | number | 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.<br> | No | 0 |
-| logit_bias | object | Modify the likelihood of specified tokens appearing in the completion.<br><br>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](https://platform.openai.com/tokenizer?view=bpe) 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.<br><br>As an example, you can pass `{"50256": -100}` to prevent the <&#124;endoftext&#124;> token from being generated.<br> | No | None |
+| logit_bias | object | Modify the likelihood of specified tokens appearing in the completion.<br><br>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. 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.<br><br>As an example, you can pass `{"50256": -100}` to prevent the <&#124;endoftext&#124;> token from being generated.<br> | No | None |
 | logprobs | integer | 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 five 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.<br><br>The maximum value for `logprobs` is 5.<br> | No | None |
-| max_tokens | integer | The maximum number of [tokens](https://platform.openai.com/tokenizer) that can be generated in the completion.<br><br>The token count of your prompt plus `max_tokens` can't exceed the model's context length. <br> | No | 16 |
+| max_tokens | integer | The maximum number of tokens that can be generated in the completion.<br><br>The token count of your prompt plus `max_tokens` can't exceed the model's context length. <br> | No | 16 |
 | n | integer | How many completions to generate for each prompt.<br><br>**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`.<br> | No | 1 |
 | presence_penalty | number | 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.<br> | No | 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.<br><br>Determinism isn't guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend.<br> | No |  |
@@ -4568,9 +4568,9 @@ Information about the content filtering category (hate, sexual, violence, self_h
 | best_of | integer | Generates `best_of` completions server-side and returns the "best" (the one with the highest log probability per token). Results can't be streamed.<br><br>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`.<br><br>**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`.<br> | No | 1 |
 | echo | boolean | Echo back the prompt in addition to the completion<br> | No | False |
 | frequency_penalty | number | 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.<br> | No | 0 |
-| logit_bias | object | Modify the likelihood of specified tokens appearing in the completion.<br><br>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](https://platform.openai.com/tokenizer?view=bpe) 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.<br><br>As an example, you can pass `{"50256": -100}` to prevent the <&#124;endoftext&#124;> token from being generated.<br> | No | None |
+| logit_bias | object | Modify the likelihood of specified tokens appearing in the completion.<br><br>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. 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.<br><br>As an example, you can pass `{"50256": -100}` to prevent the <&#124;endoftext&#124;> token from being generated.<br> | No | None |
 | logprobs | integer | 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.<br><br>The maximum value for `logprobs` is 5.<br> | No | None |
-| max_tokens | integer | The maximum number of [tokens](https://platform.openai.com/tokenizer) that can be generated in the completion.<br><br>The token count of your prompt plus `max_tokens` can't exceed the model's context length. <br> | No | 16 |
+| max_tokens | integer | The maximum number of tokens that can be generated in the completion.<br><br>The token count of your prompt plus `max_tokens` can't exceed the model's context length. <br> | No | 16 |
 | n | integer | How many completions to generate for each prompt.<br><br>**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`.<br> | No | 1 |
 | presence_penalty | number | 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.<br> | No | 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.<br><br>Determinism isn't guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend.<br> | No |  |