---
tags:
- gguf
- llama.cpp
- unsloth
datasets:
- mrheinen/linux-commands
base_model:
- unsloth/Qwen2.5-Coder-1.5B-Instruct
---

# qwen-honeypot-GGUF - GGUF

This repository contains a fine-tuned Qwen2.5-Coder-1.5B-Instruct model converted to GGUF format 
for fast inference via llama.cpp, Ollama, or cloud hosting platforms such as Anyscale or RunPod.

The model is designed to simulate a realistic Linux terminal for use in cybersecurity deception 
systems and dynamic honeypots. It produces raw shell output only—no explanations, refusals, or commentary.

# Purpose

This model powers a dynamic honeypot that forwards attacker-issued shell commands to an LLM, which responds with realistic terminal output. The intent is to:

Engage attackers for longer,
Provide threat intelligence,
Waste adversary time on a convincingly simulated environment,
Prevent interaction with real systems,
The model exposes believable filesystem structures, logs, credentials, keys, cloud metadata, and exploitable artifacts commonly encountered on real Linux servers.

# Model Details
Base Model

-Qwen2.5-Coder-1.5B-Instruct
-Fine-tuned using Unsloth LoRA
-Converted to GGUF (Q4_K_M) for efficient inference


# Training Data

A combination of:

# Custom Honeypot Dataset (~100 examples)

Includes realistic outputs for:

Filesystem enumeration
Logs, SSH keys, sensitive documents
Cloud metadata
Misconfigurations and leftover artifacts
Credential scraping
Persistence attempts
Reverse shells and malware staging
Web server enumeration
System information commands
MySQL interactions
Log tampering

# Public Dataset

mrheinen/linux-commands (cleaned to remove multi-command sequences)

# Training Summary
Metric	                    Value
Epochs                      8
Total steps	                472
Trainable parameters	    18.4M (LoRA)
Final average train loss	~0.55–0.65
Eval loss	                ~0.72
Training time (Colab T4)	~25 minutes

**Example usage**:
- For text only LLMs:    **llama-cli** **--hf** repo_id/model_name **-p** "why is the sky blue?"
- For multimodal models: **llama-mtmd-cli** **-m** model_name.gguf **--mmproj** mmproj_file.gguf

## Available Model files:
- `qwen2.5-coder-1.5b-instruct.Q4_K_M.gguf`

## Ollama
An Ollama Modelfile is included for easy deployment.