Foundation AI at Cisco developed Foundation-Sec-8B-Instruct, an 8-billion parameter instruction-tuned language model built on the Meta Llama-3.1-8B architecture. This model is specialized for cybersecurity applications, leveraging prior training in security concepts and practices. It excels at instruction-following for tasks like SOC acceleration, proactive threat defense, and engineering enablement, designed for local deployment in security-sensitive environments. The model demonstrates significant gains over Llama-3.1-8B-Instruct on security-specific benchmarks and competitive performance against models like GPT-4o-mini.
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fdtn-ai/Foundation-Sec-8B-InstructMost commonly used values from Featherless users
temperature
This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.
top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
presence_penalty
This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.
repetition_penalty
This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.
min_p
This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.