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Foundation-Sec-8B (Llama-3.1-FoundationAI-SecurityLLM-base-8B) is an open-weight, 8-billion parameter base language model specialized for cybersecurity applications. It extends Llama-3.1-8B model through continued pretraining on a curated corpus of cybersecurity-specific text, including threat intelligence reports, vulnerability databases, incident response documentation, and security standards. It has been trained to understand security concepts, terminology, and practices across multiple security domains. The model is designed to serve as a domain-adapted base model for use in applications such as threat detection, vulnerability assessment, security automation, and attack simulation. Foundation-Sec-8B enables organizations to build AI-driven security tools that can be deployed locally, reducing dependency on cloud-based AI services while maintaining high performance on security-related tasks. Foundation-Sec-8B is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications. Foundation-Sec-8B is optimized for three core use case categories: The model is intended for local deployment in environments prioritizing data security, regulatory compliance, and operational control.

Text generation
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Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct (Foundation-Sec-8B-Instruct) is an open-weight, 8-billion parameter instruction-tuned language model specialized for cybersecurity applications. It extends the Foundation-Sec-8B base model with instruction-following capabilities. It leverages prior training to understand security concepts, terminology, and practices across multiple security domains. Further instruction-tuning allows the model to interact with human users in a chat-like interface. Foundation-Sec-8B-Instruct enables organizations to build AI-driven security tools that can be deployed locally, reducing dependency on cloud-based AI services while maintaining high performance on security-related tasks. Foundation-Sec-8B-Instruct is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications. Foundation-Sec-8B-Instruct is optimized for three core use case categories: The model is intended for local deployment in environments prioritizing data security, regulatory compliance, and operational control.

Text generation
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**This model was quantized from fdtn-ai/Foundation-Sec-8B-Reasoning to a 4-bit (Q4_K_M) GGUF checkpoint using llama.cpp. It retains the cybersecurity specialization of the original 8-billion-parameter model while reducing the memory footprint from approximately 16GB (BF16) to around 4.92GB (Q4_K_M) for inference.** `fdtn-ai/Foundation-Sec-8B-Reasoning-Q4_K_M-GGUF` is a 4-bit quantized variant of **Foundation-Sec-8B-Reasoning** — an 8B-parameter LLaMA 3.1–based model that extends the **Foundation-Sec-8B** base model with instruction-following and reasoning capabilities. The base model was continued-pretrained on a curated corpus of cybersecurity-specific text (e.g., CVEs, threat intel reports, exploit write-ups, compliance guides). Foundation-Sec-8B-Reasoning is optimized for three core use case categories: Rather than re-uploading or replicating the entire training details, please refer to the original model card for foundational architecture, training data, evaluation results, and known limitations.

Text generation
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