CyberStag-Security-26B-A4B-V1 (Cybersecurity Reasoning Text Model)
[日本語版は下に記載しています / Japanese version is available below]
Overview
CyberStag-Security-26B-A4B-V1 is a highly specialized, text-only large language model designed explicitly for cybersecurity analysis and deep logical reasoning. Built upon the robust Gemma-4-26B-A4B architecture (MoE), this model has been fine-tuned using mlx_lm.lora to excel in understanding complex security domains. The LoRA adapters have been successfully merged into the base model for seamless deployment.
This model serves as a powerful reasoning engine for security professionals, researchers, SOC analysts, and system administrators. It enhances capabilities in threat modeling, vulnerability assessment, interpreting obscure system logs, and structuring comprehensive incident response strategies.
Note: This is a text-only model and does not process image, audio, or multi-modal inputs.
Performance Improvements
Comparative testing against the base model in cybersecurity contexts has confirmed the following improvements:
Enhanced Response Quality: The model provides more accurate, context-aware, and actionable responses to intricate cybersecurity queries, excelling at decomposing complex attack vectors and suggesting robust mitigation strategies.
Deeper Reasoning: We have observed a slight increase in "thinking time" (processing latency). This trade-off allows for profound, step-by-step logical deductions, yielding higher-quality analytical outputs compared to the base model.
Training Details
The model was fine-tuned on a carefully curated, distilled dataset focusing on advanced cybersecurity concepts, precise threat intelligence, logical deduction, and complex security scenarios.
(Note: To maintain model integrity and prevent misuse, the granular details of the training data remain confidential.)
Fine-Tuning Strategy & LoRA Configuration
Base Model:Gemma-4-26B-A4B (MoE)
Training Method:mlx_lm LoRA (Low-Rank Adaptation), fully merged.
Optimization Strategy: Targeting was broad and strategic, updating comprehensive Attention and MLP mechanisms (including q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and expert layers). This approach, combined with optimized context management, captures the nuances of cybersecurity terminology and code structures without catastrophic forgetting.
Available Models
We provide multiple formats to suit various hardware environments.
CyberStag-Security-26B-A4B-V1-Q4_K_M-GGUF : 4-bit quantized (Recommended for limited VRAM).
Usage
MLX
python
1from mlx_lm import load, generate
23model, tokenizer = load("deer-sec/CyberStag-Security-26B-A4B-V1-OptiQ8-MLX")4prompt ="Analyze the following server access log for potential SQL injection vulnerabilities: [LOG DATA]"5response = generate(model, tokenizer, prompt=prompt, max_tokens=4096)6print(response)
llama.cpp (GGUF)
./main -m CyberStag-Security-26B-A4B-V1-Q4_K_M.gguf -n 4096 -p "Explain the mechanics of a CSRF attack and effective mitigation strategies."
License
This model inherits the license of the base model and is provided under the Apache License 2.0. Commercial use, modification, and redistribution are freely permitted.
Disclaimer
This model is provided solely for educational, defensive research, and system resilience purposes. Usage for unauthorized access, exploit generation, or any cybercrime is strictly prohibited. By downloading/utilizing this model, you agree to employ it only for ethical security research.
