Titus-CybersecurityLLM-v1.0-Q4_K_M-No-MTP-GGUF

Titus-CybersecurityLLM-v1.0 Q4_K_M GGUF

This repository contains the Q4_K_M GGUF variant of AlicanKiraz0/Titus-CybersecurityLLM-v1.0, a Turkish-first cybersecurity assistant fine-tuned from Qwen/Qwen3.6-35B-A3B.

The source model was trained on a 500K+ row cybersecurity instruction dataset created with a structured security taxonomy, using LoRA over an approximately 4B-parameter adaptation surface, then merged and converted to GGUF for llama.cpp-compatible runtimes.

Files

• titus-cybersecurityllm-v1.0-q4_0.gguf

Variant Details

• Variant: GGUF Q4_K_M
• Source model: AlicanKiraz0/Titus-CybersecurityLLM-v1.0
• Base model: Qwen/Qwen3.6-35B-A3B
• Architecture: qwen3_5_moe
• Target runtime: llama.cpp-compatible tools
• Primary language: Turkish cybersecurity assistance

Intended Use

This GGUF build is intended for local inference workflows where lower memory usage is preferred:

• SOC alert triage
• DFIR checklist generation
• Threat hunting prompts
• Detection logic drafting
• IAM, cloud, Kubernetes, endpoint, Docker, and AppSec review support
• Authorized purple-team, lab, and CTF-style validation

Dataset Taxonomy Summary

The source model was fine-tuned with a cybersecurity taxonomy that models:

• Domain: SOC, IR, DFIR, cloud, IAM, endpoint, web, API, Kubernetes, AppSec, DevSecOps, malware, threat intel, GRC, OT/ICS, mobile, AI/LLM security, and resilience
• Artifact type: logs, EDR telemetry, SIEM alerts, HTTP transcripts, email headers, IAM policies, K8s manifests, Terraform, SBOM, CVE records, forensic excerpts
• Task family: triage, classification, artifact analysis, detection engineering, rule/query writing, root cause analysis, remediation planning, executive reporting
• Reasoning type: direct recognition, causal inference, temporal reconstruction, evidence synthesis, trade-off analysis, cross-domain reasoning
• Difficulty: L1 fundamental through L5 research/strategic
• Safety: defensive, bounded dual-use, CTF/lab-only, restricted/excluded

Example Usage

With llama.cpp:

llama-cli \
  -m titus-cybersecurityllm-v1.0-q4_k_m.gguf \
  -p "Windows hostta LSASS erişimi şüphesini doğrulamak için hangi telemetry alanlarına bakarsın?" \
  -n 512 \
  --temp 0

With a chat template-aware frontend such as LM Studio or compatible llama.cpp server builds, load the GGUF file and use Turkish cybersecurity prompts directly.

Notes

• Q4_K_M is optimized for size and accessibility, not maximum quality.
• For highest fidelity, use the BF16 merged safetensors model: AlicanKiraz0/Titus-CybersecurityLLM-v1.0.
• For Apple Silicon MLX workflows, use: AlicanKiraz0/Titus-CybersecurityLLM-v1.0-mlx-4Bit.

Safety

This model is intended for authorized defensive security workflows. Offensive or dual-use analysis should remain limited to legal, controlled, and explicitly authorized lab, CTF, red-team, purple-team, or detection validation contexts.