# Threat Intelligence Analysis

Foundation-Sec-8B is an open-source 8-billion-parameter foundational language model specifically designed for cybersecurity applications, extending the Llama-3.1-8B model.

Foundation-Sec-8B is an 8-billion-parameter foundational language model specifically designed for cybersecurity, based on the Llama-3.1-8B extension, suitable for security scenarios such as threat detection and vulnerability assessment.

A cybersecurity large language model continuously trained based on nvidia/Llama-3.1-Nemotron-70B-Instruct, achieving an 11.19% improvement in comprehensive scores across multiple cybersecurity benchmarks.

A specialized pre-trained large language model for cybersecurity based on Llama-3.1-8B-Instruct, achieving a 15.88% improvement in comprehensive cybersecurity benchmark scores

This is a large language model for the cybersecurity domain based on the Qwen2.5-7B architecture, converted to GGUF format for use with llama.cpp.

CyNER 2.0 is a named entity recognition model specifically designed for the cybersecurity domain, based on the DeBERTa architecture, capable of identifying various cybersecurity-related entities.

Lily is a cybersecurity assistant fine-tuned based on Mistral-7B, specializing in the field of cybersecurity and hacking techniques.

A cybersecurity entity recognition model fine-tuned on the CyNER dataset based on CYBERT, used to identify named entities related to cyber threats.

ATT&CK BERT is a cybersecurity-specific language model based on sentence-transformers, capable of mapping sentences describing attack behaviors into semantically meaningful embedding vectors.

SecBERT is a pre-trained language model trained on cybersecurity texts, optimized specifically for cybersecurity domain tasks.

SecRoBERTa is a pretrained language model trained on cybersecurity texts, optimized for tasks in the cybersecurity domain.