Titus-CybersecurityLLM-v1.0 MLX 4-bit
This repository contains the MLX 4-bit Apple Silicon 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 built from a multi-dimensional security taxonomy, with an approximately 4B-parameter LoRA adaptation surface, then merged and converted for efficient local inference.
Variant Details
- Variant: MLX 4-bit
- Source model:
AlicanKiraz0/Titus-CybersecurityLLM-v1.0 - Base model:
Qwen/Qwen3.6-35B-A3B - Architecture:
qwen3_5_moe - Target runtime: Apple Silicon with MLX
- Primary language: Turkish cybersecurity assistance
Intended Use
Use this variant for local Apple Silicon workflows such as:
- SOC triage assistance
- DFIR checklists and investigation notes
- Detection engineering support
- Cloud, Kubernetes, IAM, endpoint, and AppSec reviews
- GRC and control-mapping drafts
- Authorized lab, CTF, purple-team, and defensive validation tasks
Dataset Taxonomy Summary
The source model was fine-tuned with a structured cybersecurity taxonomy covering:
- Security architecture, Zero Trust, hardening, and risk terminology
- Network, web, API, IAM, endpoint, cloud, Kubernetes, and DevSecOps security
- SOC, SIEM, detection engineering, SOAR, alert tuning, and telemetry gap analysis
- Incident response, DFIR, malware analysis, threat intelligence, and threat hunting
- Vulnerability management, offensive security in authorized settings, GRC, OT/ICS, mobile, AI/LLM security, and resilience
The dataset taxonomy combines:
- Domains and subdomains
- Task families: triage, artifact analysis, detection authoring, root cause analysis, remediation planning, executive summaries
- Artifacts: logs, EDR telemetry, SIEM alerts, IAM policies, K8s manifests, code/config snippets, email headers, CVE records, forensic excerpts
- Difficulty levels: L1 through L5, from fundamentals to expert strategic reasoning
- Safety labels: defensive, bounded dual-use, CTF/lab-only, and restricted/excluded
Example Usage
Install MLX tooling:
pip install -U mlx-lm
Generate:
mlx_lm.generate \
--model AlicanKiraz0/Titus-CybersecurityLLM-v1.0-mlx-4Bit \
--prompt "Windows hostta LSASS erişimi şüphesini doğrulamak için hangi telemetry alanlarına bakarsın?" \
--max-tokens 512 \
--temp 0
Notes
- This is a quantized convenience build for Apple Silicon inference.
- For maximum fidelity, use the BF16 merged safetensors model:
AlicanKiraz0/Titus-CybersecurityLLM-v1.0. - For CPU/GPU llama.cpp-style workflows, use the GGUF variant:
AlicanKiraz0/Titus-CybersecurityLLM-v1.0-Q4_0-GGUF.
Safety
This model is intended for authorized cybersecurity work. Use offensive or dual-use outputs only in controlled, legal, and explicitly authorized lab, CTF, red-team, purple-team, or detection validation contexts.