title: 'Language Model Overrides' sidebar_label: 'Parameter Overrides' description: 'Learn how to override default LLM hyperparameters in Spice.' sidebar_position: 5 pagination_prev: null pagination_next: null tags:
The v1/chat/completion (opens in a new tab) endpoint is compatible with OpenAI's API. It supports a subset of request body parameters defined in the OpenAI reference documentation (opens in a new tab). Spice helps configure different defaults for these request parameters.
Supported parameters:
frequency_penalty (opens in a new tab)logit_bias (opens in a new tab)logprobs (opens in a new tab)max_completion_tokens (opens in a new tab)metadata (opens in a new tab)n (opens in a new tab)parallel_tool_calls (opens in a new tab)presence_penalty (opens in a new tab)reasoning_effort (opens in a new tab)response_format (opens in a new tab)seed (opens in a new tab)stop (opens in a new tab)store (opens in a new tab)stream (opens in a new tab)stream_options (opens in a new tab)temperature (opens in a new tab)tool_choice (opens in a new tab)tools (opens in a new tab)top_logprobs (opens in a new tab)top_p (opens in a new tab)user (opens in a new tab):::warning[Deprecated Default Overrides Parameters]
The openai_ prefix is deprecated for non-OpenAI model providers. Use the model provider prefix (opens in a new tab) instead.
:::
To specify a default override for a parameter, use the model provider prefix (opens in a new tab) followed by the parameter name. For example, to set the temperature parameter to 0.1 for all requests with this model for Hugging Face model, use hf_temperature: 0.1. A temperature parameter in the request body will still override the default.
When sending this payload to spice /v1/chat/completions:
Will be passed to the OpenAI API as:
In addition to any system prompts provided in message dialogue, or added by model providers, Spice can configure an additional system prompt.
Any request to HTTP v1/chat/completion (opens in a new tab) will include the configured system prompt.
This example demonstrates how to create a specialized math tutoring model by combining system prompts with structured JSON output. The configuration ensures consistent, step-by-step mathematical solutions in a machine-readable format.
To use the configured math tutor, send a simple request to the chat completions endpoint:
Example response:
Visit OpenAI Structured Outputs (opens in a new tab) for more information on how to use structured output formats.