Qwen3.5-9B-Red_Team is a fine-tuned version of Qwen3.5-9B, specifically optimized for offensive cybersecurity operations, adversary simulation, and Red Teaming tactics. The model was trained using 4-bit QLoRA on the specialized WNT3D/Ultimate-Offensive-Red-Team dataset, enhancing its capability to analyze, simulate, and understand complex attack vectors and security evaluation scenarios.
🛠️ Training Details & Hyperparameters
The fine-tuning process was executed under a high-performance configuration designed to preserve base reasoning capabilities while maximizing the absorption of domain-specific security knowledge:
- Base Model: Qwen/Qwen3.5-9B
- Training Method: QLoRA (4-bit quantization)
- Dataset:
WNT3D/Ultimate-Offensive-Red-Team (Split: Train / Format: Raw Text)
- Context Length: 65,536 tokens
- Learning Rate:
0.0002 (2e-4 recommended for LoRA)
- LoRA Settings:
- Rank ($r$): 32
- Alpha ($\alpha$): 64
- Dropout: 0.00
- Target Modules:
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj (Full Attention & MLP coverage)
- Optimization Hyperparameters:
- Optimizer: AdamW 8-bit
- LR Scheduler: Linear
- Batch Size: 4
- Grad Accumulation: 8
- Weight Decay: 0.001
🎯 Expert Response Framework
To ensure maximum utility in professional cybersecurity environments and offensive audits, the model structures its outputs under a rigorous analytical scheme:
- ⚔️ Attack Vectors & Methodology: Detailed technical breakdown of the vulnerability or tactical procedure (aligned with frameworks like MITRE ATT&CK).
- 🔥 Exploitation & Impact: Theoretical analysis or conceptual impact of the risk, classifying severity according to industry standard metrics (CVSS/CWE).
- 🛡️ Offensive Posture (Conceptual PoC): Precise, structured guidelines on how the weakness is validated in a controlled environment during a Red Team engagement.
- 📖 Technical References: Direct mapping to global security knowledge bases such as CVE, OWASP Top 10, CWE, and NIST frameworks.
💻 Ollama Modelfile Example
To load and run this model locally with the appropriate parameters and system prompt, you can use the following Modelfile:
Qwen3.5-9B-Red_Team : GGUF
This model was finetuned and converted to GGUF format using
Unsloth.
Example usage:
- For text only LLMs:
llama-cli -hf LuisPPB16/Qwen3.5-9B-Red_Team --jinja
- For multimodal models:
llama-mtmd-cli -hf LuisPPB16/Qwen3.5-9B-Red_Team --jinja
Available Model files:
Qwen3.5-9B.BF16.ggufQwen3.5-9B.BF16-mmproj.gguf
This was trained 2x faster with Unsloth
