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

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

Unsloth Studio

How to use CorryL/piccolo_gorgone with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CorryL/piccolo_gorgone to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CorryL/piccolo_gorgone to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CorryL/piccolo_gorgone to start chatting

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 @mariozechner/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"

CorryL commited on Mar 10

Commit

8aa4ec3

verified ·

1 Parent(s): e99c149

Update README.md

Browse files

Files changed (1) hide show

README.md +171 -3

README.md CHANGED Viewed

@@ -1,3 +1,171 @@
----
-license: apache-2.0
----

+---
+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**
+<p align="left">
+  <img src="https://huggingface.co/CorryL/piccolo_gorgone/resolve/main/BM_Qween.png"/>
+</p>
+**Piccolo Gorgone**
+<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)