Chat Completion

Chat model ID. Use sarvam-30b (64K context) or sarvam-105b (128K context).

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.

If set to true, the model response data will be streamed to the client as it is generated using server-sent events.

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.

This feature is in Beta. 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.

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.

Controls which (if any) tool is called by the model.

An object specifying the format that the model must output. Setting { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which guarantees the model generates output matching the supplied JSON Schema. Setting { "type": "json_object" } enables the older JSON mode, which guarantees valid JSON but not a specific schema.