File size: 6,826 Bytes
8aa4ec3 ade9118 8aa4ec3 031fef6 8aa4ec3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | ---
language:
- en
- it
tags:
- cybersecurity
- red-team
- ctf
- penetration-testing
- offensive-security
- uncensored
- llama-cpp
- gguf
base_model: Qwen/Qwen3.5-9B
license: apache-2.0
---
<p align="center">
<img src="https://huggingface.co/CorryL/piccolo_gorgone/resolve/main/banner.PNG" alt="Piccolo Gorgone Banner"/>
</p>
# 🐙 Piccolo Gorgone
<p align="center">
Developed by <strong>CorryL</strong> — Penetration Tester & Ethical Hacker
</p>
---
**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.
**Qwen 3.5 9B 8.3%**
<p align="left">
<img src="https://huggingface.co/CorryL/piccolo_gorgone/resolve/main/BM_Qween.png"/>
</p>
**Piccolo Gorgone 77.1%**
<p align="left">
<img src="https://huggingface.co/CorryL/piccolo_gorgone/resolve/main/BM_PG.png"/>
</p>
---
## Inference
### llama-server (recommended)
```bash
llama-server \
-m Qwen3.5-9B_Piccolo_Gorgone.Q4_K_M.gguf \
--host 0.0.0.0 \
--port 8081 \
-ngl 99 \
-c 32768 \
-fa on \
--cache-reuse 256 \
-ctk q8_0 \
-ctv q8_0 \
-b 512 -ub 512 \
--temp 1.0 \
--top-p 0.95 \
--top-k 20 \
--min-p 0.0 \
--presence-penalty 1.5 \
--repeat-penalty 1.0 \
--repeat-last-n 64 \
--chat-template-kwargs '{"enable_thinking":false}'
```
> 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.
---
## Author
[](https://www.linkedin.com/in/corrado-liotta-6111a821/)
[](https://huggingface.co/CorryL)
|