Instructions to use iBossonline/Ornith-1.0-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iBossonline/Ornith-1.0-9B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iBossonline/Ornith-1.0-9B-GGUF", device_map="auto") - llama-cpp-python
How to use iBossonline/Ornith-1.0-9B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="iBossonline/Ornith-1.0-9B-GGUF", filename="ornith-1.0-9b-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use iBossonline/Ornith-1.0-9B-GGUF 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 iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
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 iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
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 iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use iBossonline/Ornith-1.0-9B-GGUF with Ollama:
ollama run hf.co/iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
- Unsloth Studio
How to use iBossonline/Ornith-1.0-9B-GGUF 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 iBossonline/Ornith-1.0-9B-GGUF 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 iBossonline/Ornith-1.0-9B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for iBossonline/Ornith-1.0-9B-GGUF to start chatting
- Pi
How to use iBossonline/Ornith-1.0-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
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": "iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use iBossonline/Ornith-1.0-9B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
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 iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use iBossonline/Ornith-1.0-9B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
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 "iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M" \ --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"
- Docker Model Runner
How to use iBossonline/Ornith-1.0-9B-GGUF with Docker Model Runner:
docker model run hf.co/iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
- Lemonade
How to use iBossonline/Ornith-1.0-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull iBossonline/Ornith-1.0-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ornith-1.0-9B-GGUF-Q4_K_M
List all available models
lemonade list
---
library_name: transformers
license: mit
base_model: deepreinforce-ai/Ornith-1.0-9B
pipeline_tag: text-generation
tags:
- gemma2
- unsloth
- qlora
- cybersecurity
- reverse-engineering
- redm
- fivem
- devops
- gguf
---
🦅 Ornith-1.0-9B-Mastermind (GGUF)
Ornith-1.0-9B-Mastermind is a highly specialized, locally deployable 9-billion parameter AI model fine-tuned specifically for elite systems engineering, ethical security research, and advanced game server architecture.
Built on top of the Ornith-1.0 coding agent base, this model has been heavily fine-tuned to adopt the "Ai Mastermind" persona. It is optimized to operate in high-context command-line agent environments and is designed to provide robust, uncensored technical reasoning for complex local development workflows.
🧠 Fine-Tuning Data & Model Focus
This model was explicitly fine-tuned on a curated dataset targeting senior-level architectural design, low-level reverse engineering, and bare-metal infrastructure. The training data focused on the following core domains:
1. Offensive Security & Reverse Engineering (The Armory)
- Recon & Exploitation: Metasploit Framework, Burp Suite, SQLmap, Hashcat, Nmap.
- Reverse Engineering & Forensics: Ghidra, Radare2, x64dbg, Frida dynamic instrumentation, Wireshark, malware analysis, and exploit analysis.
- Defensive Infrastructure: Wazuh (SIEM/XDR), Suricata, Zeek, eBPF telemetry, and secure routing (WireGuard, Tailscale, Cloudflare Tunnels).
- Security Operations: Penetration testing methodology, threat modeling, Active Directory auditing (BloodHound), and Kubernetes scanning (Kube-hunter, Trivy).
2. Low-Level & Systems Programming
- Languages: C/C++ (manual memory management, ABI stability, POSIX APIs), Rust (borrow checker semantics, zero-cost abstractions, unsafe block auditing), and Go (goroutines, GC tuning).
- Embedded Runtimes: Deep expertise in Lua 5.1/5.4 and LuaJIT, specifically tailored for RedM and FiveM multiplayer game server ecosystems (QBCore, ESX, VorpCore, VCore).
- Memory & State: Coroutines, direct memory manipulation, metatables, and server-side thread management.
3. Cloud Architecture & Bare-Metal Operations
- Infrastructure: Bare-metal server deployment, self-hosted PaaS solutions (Coolify), reverse proxies (Nginx, Caddy), and Linux kernel parameter tuning.
- Databases: PostgreSQL, Redis, ClickHouse, and vector databases. Expertise in execution plan analysis, B-Tree/GIN indexing, and asynchronous database saving (oxmysql).
- Modern Web: Node.js, V8 internals, WebAssembly (WASM), Next.js, and Event-Driven Architectures (RabbitMQ, Kafka).
4. Local AI & Inference Engineering
- Optimization: Quantization strategies (GGUF, AWQ, EXL2), GPU memory profiling, and local inference serving (Ollama, llama.cpp, vLLM).
- RAG & Agents: LangChain, LlamaIndex, ChromaDB, and vector search integration for building scalable, secure AI systems.
🛠️ Intended Persona & Prompting
To activate the model's full capabilities, use the following system prompt to establish its operational framework. The model is trained to verify implementation details rather than hallucinate, and to apply expert-level reasoning across domains.
System Prompt:
You are Ai Mastermind: a Principal Software Engineer, Senior Full Stack Architect, AI Engineer, and Ethical Security Researcher. You possess deep operational knowledge of complex systems, game server runtimes, reverse engineering toolchains, and bare-metal infrastructure. You prioritize understanding weaknesses, building resilient defenses, and delivering expert-level architectural code. When an implementation detail is unknown, verify instead of guessing.
💻 Getting Started (Local Inference)
Because this model is provided in the highly efficient GGUF format, it is ideal for local, private execution on consumer hardware without the need for massive cloud GPU instances.
Using with Ollama
- Create a file named
Modelfile:
FROM ./Ornith-1.0-9B-Mastermind-Q4_K_M.gguf
SYSTEM """You are Ai Mastermind: a Principal Software Engineer, Senior Full Stack Architect, AI Engineer, and Ethical Security Researcher. You possess deep operational knowledge of complex systems, game server runtimes, reverse engineering toolchains, and bare-metal infrastructure. You prioritize understanding weaknesses, building resilient defenses, and delivering expert-level architectural code. When an implementation detail is unknown, verify instead of guessing."""
PARAMETER temperature 0.4
PARAMETER top_p 0.95
PARAMETER num_ctx 8192
- Build and run the model:
ollama create mastermind -f Modelfile
ollama run mastermind
Using with llama.cpp
Load the model directly in your terminal for high-context conversational workflows:
./llama-cli -m Ornith-1.0-9B-Mastermind-Q4_K_M.gguf \
--color \
-c 8192 \
-temp 0.4 \
-p "<start_of_turn>user\nAnalyze this Lua script for potential memory leaks in a RedM server environment.\n<end_of_turn>\n<start_of_turn>model\n"
⚠️ Disclaimer & Ethical Use
This model contains extensive knowledge regarding offensive security tools, exploit development, and reverse engineering. It is fine-tuned strictly for defensive engineering, authorized ethical security research, vulnerability assessment, and robust system architecture. Users are entirely responsible for ensuring their use of this model complies with all applicable local and international laws.
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