Instructions to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Use Docker

docker model run hf.co/r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Ollama
How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with Ollama:
```
ollama run hf.co/r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M
```
Unsloth Desktop

How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with Docker Model Runner:
```
docker model run hf.co/r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M
```

Lemonade

How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-3b-instruct.Q4_K_M.gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

██████╗ ███████╗███╗   ██╗████████╗███████╗███████╗████████╗     █████╗ ██╗
██╔══██╗██╔════╝████╗  ██║╚══██╔══╝██╔════╝██╔════╝╚══██╔══╝    ██╔══██╗██║
██████╔╝█████╗  ██╔██╗ ██║   ██║   █████╗  ███████╗   ██║       ███████║██║
██╔═══╝ ██╔══╝  ██║╚██╗██║   ██║   ██╔══╝  ╚════██║   ██║       ██╔══██║██║
██║     ███████╗██║ ╚████║   ██║   ███████╗███████║   ██║       ██║  ██║██║
╚═╝     ╚══════╝╚═╝  ╚═══╝   ╚═╝   ╚══════╝╚══════╝   ╚═╝       ╚═╝  ╚═╝╚═╝

⚡ 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.

🎯 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

🚀 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.

⚙️ Optimal Settings

Parameter	Value	Notes
`temperature`	`1.0`	Good creative range
`top_p`	`0.95`	Balanced sampling
`top_k`	`40`	Optional
`repeat_penalty`	`1.1`	Prevents loops
`max_tokens`	`1024–4096`	Longer = more detailed
`context`	`4096`	Recommended minimum

💻 Hardware Requirements

Setup	Minimum VRAM/RAM	Speed
GPU (CUDA/Metal)	4 GB VRAM	🚀 Fast (30–60 tok/s)
CPU only	8 GB RAM	🐢 Slow (2–5 tok/s)
Apple Silicon	8 GB unified	⚡ Very fast

🏗️ 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

📊 Model Card Info

Property	Value
Architecture	Qwen2.5 (transformer)
Parameters	3B total
Context Length	32,768 tokens (trained)
Quantization	Q4_K_M GGUF
File Size	~2 GB
Language	English
Domain	Cybersecurity / Security Research

📝 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

⚠️ 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

Built with 🖤 for the security research community

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

Downloads last month: 1,093

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

unsloth/Qwen2.5-3B-Instruct

Quantized

(13)

this model

r1r21nb
/

qwen2.5-3b-instruct.Q4_K_M.gguf

⚡ Pentest AI — 3B Security Research Model

🎯 What Is This?

🚀 Quick Start

Option 1 — llama.cpp (Fastest)

Option 2 — Python (llama-cpp-python)

Option 3 — Ollama

Option 4 — LM Studio / Jan / GPT4All

⚙️ Optimal Settings

💻 Hardware Requirements

🏗️ How It Was Built

📊 Model Card Info

📝 Prompt Format (ChatML)

⚠️ Intended Use

Model tree for r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf

Space using r1r21nb/qwen2.5-3b-instruct.Q4_K_M.gguf 1