DexopT/Qwen3-4B-Cybersecurity

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Qwen3-4B-Cybersecurity

Qwen3-4B fine-tuned on 1.28M cybersecurity samples

    [](https://apache.org/licenses/LICENSE-2.0)

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🔪 Abliterated (refusal-removed) version: DexopT/Qwen3-4B-Cybersecurity-Heretic-16bit

Model Description

Qwen3-4B-Cybersecurity is a fine-tuned version of Qwen3-4B-Instruct-2507 specialized in cybersecurity topics including offensive security, penetration testing, vulnerability analysis, malware analysis, and threat intelligence.

Trained with Unsloth SFT on a curated dataset of 1,284,369 samples covering:

• —🔴 Offensive Security — SQL injection, XSS, buffer overflows, reverse shells
• —🔵 Defensive Security — CVE analysis, incident response, hardening
• —🟡 Network Security — MITM, ARP spoofing, DNS poisoning, DDoS
• —🟢 Active Directory — Kerberoasting, Pass-the-Hash, Golden Ticket attacks
• —⚫ Malware Analysis — Ransomware, rootkits, persistence techniques
• —🟣 Web Security — SSRF, XXE, CSRF, LFI/RFI, deserialization

Model Family

Training Details

Datasets

Dataset sources include .jsonl, .json, .csv, .parquet, and .arrow formats from multiple cybersecurity repositories, converted to unified OpenAI messages format.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "DexopT/Qwen3-4B-Cybersecurity",
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("DexopT/Qwen3-4B-Cybersecurity")

messages = [
    {"role": "system", "content": "You are an expert cybersecurity assistant."},
    {"role": "user", "content": "Explain how SQL injection works and how to prevent it."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.8)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

MLX (Apple Silicon)

pip install mlx-lm

mlx_lm.convert \
  --hf-path DexopT/Qwen3-4B-Cybersecurity \
  --mlx-path ~/models/qwen3-cyber-mlx \
  --quantize --q-bits 8

mlx_lm.chat --model ~/models/qwen3-cyber-mlx

LM Studio / llama.cpp / Ollama

Use the GGUF version: DexopT/Qwen3-4B-Cybersecurity-GGUF

Refusal Behavior

This model retains some of the original refusal behavior from the base Qwen3 model. For a version with refusal directions removed via Heretic abliteration, see:

🔪 DexopT/Qwen3-4B-Cybersecurity-Heretic-16bit

⚠️ Disclaimer

This model is intended for educational and research purposes only. Use responsibly and only on systems you have explicit permission to test. The authors are not responsible for any misuse.

Links