qwen3-14b-redstack-v1

Zero Stack - Qwen3-14B (GGUF, Q5_K_M)

Qwen3-14B fine-tuned on an offensive-security SFT dataset (1,226 rows). Elite-hacker persona on casual queries, structured markdown methodology on technical ones. Thinking mode enabled by default (Qwen3-14B base behavior).

Files

• qwen3-14b.Q5_K_M.gguf - quantized weights (~9.8 GB)
• Modelfile - Ollama template with correct ChatML stop tokens + Zero Stack system prompt

Run with Ollama

ollama create zerostack-14b -f Modelfile
ollama run zerostack-14b

Run with llama.cpp

./llama-cli -m qwen3-14b.Q5_K_M.gguf -p "hello"

Training

• Base: Qwen3-14B
• Method: LoRA (r=32), 3 epochs, Unsloth
• Max sequence length: 2560
• Dataset: SFT_GENERALIST (1,226 rows, ChatML)

Intended Use

Authorized security testing, CTF practice, red-team research, and security education. Targeted at practitioners who already know what they're doing and want structured methodology and command recall.

Limitations & Risks

• May hallucinate specific CVE IDs, tool flags, or payload syntax - verify against primary sources before running.
• No safety guardrails against misuse. Do not use against systems you don't own or have explicit written authorization to test.
• Thinking mode is on by default - responses may be slower and include reasoning traces. Disable in Modelfile if you want faster, terser output.
• Trained on English data only; non-English performance is not evaluated.
• 16 GB VRAM note: GGUF export uses CPU offloading to avoid LoRA merge corruption. If you retrain/re-export, verify maximum_memory_usage=0.5 in export_gguf.py.

License / Use

For authorized security testing, research, and educational use only. Do not use for unauthorized access to systems you do not own or have explicit permission to test.