Update Readme

README.md

@@ -11,7 +11,62 @@ base_model:
 
 # qwen-honeypot-GGUF - GGUF
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+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?"