Qwen3.5-4B-Qwen3.8-Distill-Heretic-CyberStrike-GGUF

⚠️ IMPORTANT: This model is explicitly designed for authorized security research, offensive cybersecurity, and red teaming. By using this model, you agree to strictly adhere to the Responsible Use Guidelines outlined below.

🎯 Model Overview

Qwen3.5-4B-Qwen3.8-Distill-Heretic-CyberStrike-GGUF is a highly specialized, fine-tuned large language model optimized for offensive cybersecurity, vulnerability research, and authorized penetration testing.

Built upon the insraq/Qwen3.5-4B-EmperoAI-Qwen3.8-Distill-Heretic-Abliterated base model and fine-tuned on the extensive CyberStrike SFT 120K dataset, this GGUF-quantized model delivers expert-level, actionable technical depth without artificial safety refusals. It is explicitly designed to serve as the cognitive engine for the CyberStrike AI Framework.

🚀 Intended Use Cases

This model is purpose-built for security professionals and researchers. Recommended use cases include:

  • Authorized Penetration Testing: Generating exploitation chains, payload variations, and post-exploitation methodologies.
  • Vulnerability Research: Deep-dive CVE analysis, patch diffing, and exploit development reasoning.
  • CyberStrike Framework Integration: Powering autonomous red team agents, proxy testers, and MCP (Model Context Protocol) security tools.
  • Defensive Security & Training: Simulating advanced persistent threat (APT) tactics for blue team training and detection engineering.

📚 Training & Architecture

Component Details
Base Model insraq/Qwen3.5-4B-EmperoAI-Qwen3.8-Distill-Heretic-Abliterated
Fine-Tuning Dataset oyildirim/cyberstrike-sft-120k (121,422 expert-level red team instruction-response pairs)
Format GGUF (Optimized for efficient CPU/GPU inference via llama.cpp, Ollama, or LM Studio)
Domains Covered Cloud Security, CVE Reasoning, CIS Hardening, MITRE ATT&CK, OWASP (Web/ASVS), NIST, and General Offensive Operations
Behavior Uncensored, operator-level technical detail, framework-aligned, no generic refusals