metadata
license: other
pipeline_tag: text-generation
library_name: gguf
language:
- en
base_model: unsloth/Qwen3-4B-Instruct-2507
base_model_relation: quantized
tags:
- gguf
- qwen3
- pentesting
- security
- lora
- sft
Zero Stack - Qwen3-4B (GGUF, Q4_K_M)
Qwen3-4B-Instruct-2507 fine-tuned on an offensive-security SFT dataset (1,226 rows). Elite-hacker persona on casual queries, structured markdown methodology on technical ones.
Files
qwen3-4b-instruct-2507.Q4_K_M.gguf- quantized weights (~2.5 GB)Modelfile- Ollama template with correct ChatML stop tokens + Zero Stack system prompt
Run with Ollama
ollama create zerostack-4b -f Modelfile
ollama run zerostack-4b
Run with llama.cpp
./llama-cli -m qwen3-4b-instruct-2507.Q4_K_M.gguf -p "hello"
Training
- Base:
Qwen3-4B-Instruct-2507 - Method: LoRA (r=32), 3 epochs, Unsloth
- 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 fast recall of commands, workflows, and methodology.
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.
- Small model (4B) - shallower reasoning than the 14B; prefer 14B for multi-step planning.
- Persona responses are stylistic flavor, not a safety signal.
- Trained on English data only; non-English performance is not evaluated.
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.