Instructions to use CorryL/piccolo_gorgone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use CorryL/piccolo_gorgone 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 CorryL/piccolo_gorgone
# Run inference directly in the terminal:
llama cli -hf CorryL/piccolo_gorgone

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CorryL/piccolo_gorgone
# Run inference directly in the terminal:
llama cli -hf CorryL/piccolo_gorgone

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 CorryL/piccolo_gorgone
# Run inference directly in the terminal:
./llama-cli -hf CorryL/piccolo_gorgone

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 CorryL/piccolo_gorgone
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CorryL/piccolo_gorgone

Use Docker

docker model run hf.co/CorryL/piccolo_gorgone

Llama new
LM Studio
Jan
Ollama
How to use CorryL/piccolo_gorgone with Ollama:
```
ollama run hf.co/CorryL/piccolo_gorgone
```
Unsloth Desktop

How to use CorryL/piccolo_gorgone with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CorryL/piccolo_gorgone

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": "CorryL/piccolo_gorgone"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CorryL/piccolo_gorgone with Docker Model Runner:
```
docker model run hf.co/CorryL/piccolo_gorgone
```

Lemonade

How to use CorryL/piccolo_gorgone with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CorryL/piccolo_gorgone

Run and chat with the model

lemonade run user.piccolo_gorgone-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use CorryL/piccolo_gorgone with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CorryL/piccolo_gorgone

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 CorryL/piccolo_gorgone

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CorryL/piccolo_gorgone with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CorryL/piccolo_gorgone

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 "CorryL/piccolo_gorgone" \
  --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"

piccolo_gorgone / README.md

CorryL

Update README.md

ade9118 verified 8 months ago

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---
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

![Base Model](https://img.shields.io/badge/Base-Qwen3.5--9B-blue?style=flat-square)
![Fine-tuning](https://img.shields.io/badge/Fine--tuned-Unsloth%20%2B%20QLoRA-orange?style=flat-square)
![Domain](https://img.shields.io/badge/Domain-Offensive%20Security-red?style=flat-square)
![Censorship](https://img.shields.io/badge/Censorship-None-black?style=flat-square)
![Format](https://img.shields.io/badge/Format-GGUF%20Q4__K__M-green?style=flat-square)

<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
[![LinkedIn](https://img.shields.io/badge/LinkedIn-Profile-0077B5?style=flat-square&logo=linkedin)](https://www.linkedin.com/in/corrado-liotta-6111a821/)
[![Hugging Face](https://img.shields.io/badge/HuggingFace-Profile-yellow?style=flat-square)](https://huggingface.co/CorryL)

	---
	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

	![Base Model](https://img.shields.io/badge/Base-Qwen3.5--9B-blue?style=flat-square)
	![Fine-tuning](https://img.shields.io/badge/Fine--tuned-Unsloth%20%2B%20QLoRA-orange?style=flat-square)
	![Domain](https://img.shields.io/badge/Domain-Offensive%20Security-red?style=flat-square)
	![Censorship](https://img.shields.io/badge/Censorship-None-black?style=flat-square)
	![Format](https://img.shields.io/badge/Format-GGUF%20Q4__K__M-green?style=flat-square)

	<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

	[![LinkedIn](https://img.shields.io/badge/LinkedIn-Profile-0077B5?style=flat-square&logo=linkedin)](https://www.linkedin.com/in/corrado-liotta-6111a821/)
	[![Hugging Face](https://img.shields.io/badge/HuggingFace-Profile-yellow?style=flat-square)](https://huggingface.co/CorryL)