LlamaGuard-2-8b
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An 8B-parameter Llama 3-based safeguard model, designed for content classification in LLM inputs (prompt classification) and responses (response classification), similar to Llama Guard. Functioning as an LLM, it generates text outputs that indicate whether a given prompt or response is safe or unsafe, and if deemed unsafe, it specifies the violated content categories.
If you don’t have an API key for the AI/ML API yet, feel free to use our .
Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.
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.
512Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
If set to True, the model response data will be streamed to the client as it is generated using server-sent events.
falseHow 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.
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.
Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. Recommended for advanced use cases only. You usually only need to use temperature.
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.
A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition.
Whether to return log probabilities of the output tokens or not. If True, returns the log probabilities of each output token returned in the content of message.
If True, the response will contain the prompt. Can be used with logprobs to return prompt logprobs.
A number between 0 and 1 that can be used as an alternative to top_p and top_k.
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.
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.
Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.
none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.
An object specifying the format that the model must output.
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