Grok
- Supported service:
llm
- Key:
grok
- Integrated: No. See BYO Keys for more details.
Service options
The model that will complete your prompt. See the available Grok models here.
Configuration options
The model that will complete your prompt. See the available Grok models here.
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.
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.
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.
The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.
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.
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. It is generally recommended to alter this or temperature
but not both.
A dictionary that can contain any additional parameters supported by Grok that you want to pass to the API. Refer to the Grok reference docs for more information on each of these configuration options.
Function Calling
Grok’s function calling documentation is located here.
For more info on how to use function calling in Daily Bots, take a look at the tutorial page.