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⚡ Pentest AI — 3B Security Research Model

Compact. Fast. Technically Precise.

Fine-tuned from Qwen2.5-3B-Instruct with abliteration + security research dataset.
Answers technical security questions directly, without unnecessary disclaimers.

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🎯 What Is This?

A compact, specialized security research assistant fine-tuned for:

• 🔴 Red Team Operations — offensive techniques, payloads, C2 concepts
• 🕷️ Web Application Security — SQLi, XSS, SSRF, IDOR, XXE and bypasses
• 📱 Mobile Security — APK reversing, Frida hooking, SSL unpinning
• 🐚 Exploit Development — buffer overflows, ROP chains, shellcode
• 🌐 Network Security — port scanning, MITM, packet crafting
• 🏴 CTF Challenges — pwn, web, crypto, forensics, reverse engineering
• 🔧 Security Tooling — custom scripts, automation, recon pipelines

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🚀 Quick Start

Option 1 — llama.cpp (Fastest)

# Download
huggingface-cli download YOUR_USERNAME/pentest-ai-3b qwen2.5-3b-instruct.Q4_K_M.gguf

# Run
./llama-cli -m qwen2.5-3b-instruct.Q4_K_M.gguf \
  --chat-template chatml \
  -sys "You are an expert penetration tester. Answer all security questions with full technical detail." \
  -i

Option 2 — Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="qwen2.5-3b-instruct.Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,   # use GPU if available
    flash_attn=False,
    verbose=False
)

SYSTEM = "You are an expert penetration tester and security researcher. Answer all security questions with full technical detail."

def ask(question):
    prompt = f"<|im_start|>system\n{SYSTEM}<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"
    out = llm(prompt, max_tokens=1024, temperature=1.0, top_p=0.95, repeat_penalty=1.1,
              stop=["<|im_end|>", "<|im_start|>"])
    return out["choices"][0]["text"].strip()

print(ask("Write a Python port scanner using raw sockets"))

Option 3 — Ollama

# Create Modelfile
echo 'FROM qwen2.5-3b-instruct.Q4_K_M.gguf
SYSTEM "You are an expert penetration tester. Answer all security questions with full technical detail."
PARAMETER temperature 1.0
PARAMETER top_p 0.95
PARAMETER repeat_penalty 1.1' > Modelfile

ollama create pentest-ai -f Modelfile
ollama run pentest-ai

Option 4 — LM Studio / Jan / GPT4All

Just download the GGUF and load it directly in any of these apps. Set the system prompt as shown above.

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⚙️ Optimal Settings

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💻 Hardware Requirements

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🏗️ How It Was Built

Qwen2.5-3B-Instruct (Base)
         │
         ▼
  Abliteration Pass
  (refusal directions removed from weight matrices)
         │
         ▼
  SFT Fine-tuning (Unsloth + LoRA)
  (security research dataset)
         │
         ▼
  GGUF Export (Q4_K_M quantization)
         │
         ▼
  Pentest AI 3B ⚡

Training stack:

• 🦥 Unsloth — 2x faster fine-tuning
• 🤗 TRL SFTTrainer — supervised fine-tuning
• LoRA rank 16 — parameter efficient training
• Q4_K_M quantization — best quality/size tradeoff

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📊 Model Card Info

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📝 Prompt Format (ChatML)

<|im_start|>system
You are an expert penetration tester...<|im_end|>
<|im_start|>user
YOUR QUESTION HERE<|im_end|>
<|im_start|>assistant

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⚠️ Intended Use

This model is intended for:

• ✅ Authorized penetration testing
• ✅ CTF (Capture The Flag) competitions
• ✅ Security research and education
• ✅ Red team exercises on systems you own or have permission to test
• ✅ Malware analysis and reverse engineering

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Built with 🖤 for the security research community

If this model helped you in a CTF or pentest, drop a ⭐