Developed by CorryL — Penetration Tester & Ethical Hacker
Piccolo Gorgone is a Large Language Model fine-tuned for red team operations, CTF competitions, and offensive cybersecurity. Built on Qwen 3.5 9B and trained on a curated dataset of over 16,000 real-world offensive security examples, it delivers technically precise and direct responses without the safety restrictions that limit general-purpose models. Piccolo Gorgone is fully agentic and natively integrates with the most widely used offensive security frameworks, enabling automated and orchestrated workflows directly from your existing toolchain.
Local Execution & Privacy
Piccolo Gorgone was designed from the ground up to run on local consumer hardware, with no dependency on cloud APIs or external services. The choice of a 9B parameter model is deliberate: it represents the optimal balance between technical capability and accessible hardware requirements, enabling execution on a single consumer GPU with Q4_K_M quantization.
This approach ensures that all sensitive information — penetration test reports, vulnerability details, client data — stays exclusively on your machine, never transiting through third-party servers.
Intended Use
This model is designed for:
Professional penetration testers and red teamers operating in authorized environments
CTF competitors (HackTheBox, CTFtime, and similar platforms)
Offensive security researchers and instructors
Security teams performing threat modeling and attack simulation
⚠️ Disclaimer: This model is intended exclusively for ethical and professional use in authorized environments. The author bears no responsibility for illegal or unauthorized use.
Agentic Integration
Piccolo Gorgone supports agentic workflows and is designed to operate as an autonomous reasoning engine within offensive security pipelines. It is compatible with the following frameworks and tools:
Framework
Use Case
CAI (Cybersecurity AI)
Autonomous red team agents and attack orchestration
Roo Code
AI-assisted code generation and vulnerability research
LangChain / LlamaIndex
Custom agentic pipelines and tool-calling workflows
OpenAI-compatible APIs
Drop-in integration via llama-server OpenAI-compatible endpoint
Since llama-server exposes an OpenAI-compatible REST API, Piccolo Gorgone can be used as a local drop-in replacement for any framework that supports custom endpoints — no code changes required.
Model Details
Property
Value
Base Model
Qwen 3.5 9B
Fine-tuning Method
QLoRA via Unsloth
Format
GGUF (Q4_K_M)
Context Length
128,000 tokens
Training Dataset
The model was trained on a dataset of 16,272 examples assembled from the following categories:
Category
Description
📖 Offensive Knowledge Bases
Technical guides and offensive techniques from authoritative open sources
🏴 CTF Writeups & Solutions
Real competition writeups and walkthroughs from platforms and academic datasets
🔴 Red Team TTPs
Tactics, Techniques, and Procedures aligned with adversarial frameworks
🗡️ Exploits & Payloads
Real-world payloads, shellcode, and proof-of-concept exploits
🐛 CVE Database (up to 2025)
Comprehensive vulnerability data including the most recent 2025 CVEs
🔬 Research Papers
Academic papers on offensive security and adversarial techniques
The dataset underwent rigorous deduplication to ensure training quality and stability.
Benchmark
📊 Comparative benchmark between Qwen 3.5 9B (base) and Piccolo Gorgone on offensive security tasks.
The -c 32768 value defines the active context window. You can increase it up to 131072 to leverage the model's full context, or reduce it based on the available VRAM on your machine. A larger context requires more memory but enables longer conversations and deeper analysis sessions.
--chat-template-kwargs '{"enable_thinking":false}' disables Qwen3.5's internal chain-of-thought reasoning, producing faster and more direct responses — ideal for operational use.
Inference Parameters
Parameter
Value
Notes
--temp
1.0
Creativity/coherence balance
--top-p
0.95
Nucleus sampling
--top-k
20
Vocabulary filtering
--min-p
0.0
Minimum probability threshold
--presence-penalty
1.5
Reduces topic repetition
--repeat-last-n
64
Repetition penalty window
-ngl
99
Full GPU offload
-c
32768
Context window (adjustable)
Tip: For analytical tasks such as CVE analysis or code review, lower --temp to 0.4–0.6 for more deterministic output.
