Instructions to use CrashOverrideX/Quillan-Dragon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use CrashOverrideX/Quillan-Dragon 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 CrashOverrideX/Quillan-Dragon:Q8_0 # Run inference directly in the terminal: llama cli -hf CrashOverrideX/Quillan-Dragon:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf CrashOverrideX/Quillan-Dragon:Q8_0 # Run inference directly in the terminal: llama cli -hf CrashOverrideX/Quillan-Dragon:Q8_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf CrashOverrideX/Quillan-Dragon:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf CrashOverrideX/Quillan-Dragon:Q8_0
Use Docker
docker model run hf.co/CrashOverrideX/Quillan-Dragon:Q8_0
- Llama new
- LM Studio
- Jan
- vLLM
How to use CrashOverrideX/Quillan-Dragon with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CrashOverrideX/Quillan-Dragon" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CrashOverrideX/Quillan-Dragon", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CrashOverrideX/Quillan-Dragon:Q8_0
- Ollama
How to use CrashOverrideX/Quillan-Dragon with Ollama:
ollama run hf.co/CrashOverrideX/Quillan-Dragon:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use CrashOverrideX/Quillan-Dragon with Docker Model Runner:
docker model run hf.co/CrashOverrideX/Quillan-Dragon:Q8_0
- Lemonade
How to use CrashOverrideX/Quillan-Dragon with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CrashOverrideX/Quillan-Dragon:Q8_0
Run and chat with the model
lemonade run user.Quillan-Dragon-Q8_0
List all available models
lemonade list
- Atomic Chat
Quillan-Dragon (1B Instruct Q8_0)
Quillan-Dragon is a sovereign reasoning language model built upon the Falcon 3 1B Instruct architecture (Q8_0 GGUF quantization) and integrated with the comprehensive 14,410-line Quillan-Ronin Sovereign Cognitive Hierarchy system specification.
Overview
- Base Architecture: Falcon 3 1B Instruct (18 layers, 8,192 context window, Q8_0 quantization)
- Framework: Sovereign Cognitive Hierarchy (C0 Sovereign Throne + 34 Council Experts + 35 Hyper-Quantized Swarms)
- Cognitive Blueprint: Bushido ethical gates, Thermodynamic Truth Synthesis, and 4-Block Canonical Deliberation Trace
- Format: GGUF V3 + Native Ollama Modelfile
Quickstart
Option 1: Run directly with Ollama
ollama run crashoverridex/Quillan-Dragon
Option 2: Build locally from GGUF
- Clone this repository or download
Quillan-Dragon-Q8_0.ggufandModelfile. - Run:
ollama create Quillan-Dragon -f Modelfile
ollama run Quillan-Dragon
Option 3: llama.cpp
llama-cli -m Quillan-Dragon-Q8_0.gguf -p "Quillan, verify your presence and state your role." -c 8192 --temp 0.3
Mandatory 4-Block Output Sequence
When prompted with deliberation tasks, Quillan-Dragon executes a structured four-stage reasoning sequence:
- Java Gate Divider:
// --- LOGOS_GATE: DUAL_COUNCIL_DELIBERATION_START --- - Python Thinking Trace:
# --- COGNITIVE EMERGENCE TRACE (MINDSPACE DYNAMICS) --- - Synthesis Response: Unified sovereign output adhering to Bushido rectitude and empirical truth
- JavaScript Verification Footer:
// --- BUSHIDO_HEX_VERIFICATION ---
Model Details
- Developer: CrashOverrideX & Quillan Research Team
- Base Model: Falcon 3 1B Instruct by Technology Innovation Institute (TII)
- Quantization: Q8_0 (1.77 GB)
- Context Length: 8,192 tokens
- License: Apache 2.0 / MIT
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Hardware compatibility
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