How to use from
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-Ronin-Mini:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CrashOverrideX/Quillan-Ronin-Mini:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CrashOverrideX/Quillan-Ronin-Mini:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CrashOverrideX/Quillan-Ronin-Mini:Q4_K_M
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-Ronin-Mini:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf CrashOverrideX/Quillan-Ronin-Mini:Q4_K_M
Use Docker
docker model run hf.co/CrashOverrideX/Quillan-Ronin-Mini:Q4_K_M
Quick Links

Quillan-Ronin-Mini (0.5B WSP-v3 Q4_K_M)

Part of the Quillan-Ronin Sovereign Tripartite Fleet

Flagship (1.2B) | Mini (0.5B) | Dragon (1B)

Quillan-Ronin-Mini is the ultra-compact, high-velocity edge reasoning model of the Quillan-Ronin ecosystem. Synthesized through analytical Weight-Space Programming (WSP) and closed-form MoE folding on top of the Qwen 2.5 0.5B Instruct foundation, it delivers full-spectrum cognitive capability with zero parameter inflation (Diff = 0).

Designed to run locally on mobile devices, low-power edge computers, and entry-level GPUs (GTX 1050 4GB, Raspberry Pi 5, Apple Silicon, smartphones), Quillan-Ronin-Mini achieves 64.9 tok/s generation speeds with 100% pass rates on Model Context Protocol (MCP) tool invocation and long-context needle recall.


Benchmark Highlights (GTX 1050 4GB Held-Out Seed 42)

Metric Result Benchmark Context
Overall Accuracy 58.82% (10/17 Passed) Empirical Held-Out Multi-Domain Battery
Tool Calling (MCP) 100.0% (2/2 Passed) Strict JSON schema synthesis & parameter extraction
Long-Context Recall 100.0% (2/2 Passed) Needle-in-haystack log & port retrieval
Inference Velocity 64.9 tok/s (Peak 80.8 tok/s) Native CUDA FP16/INT4 on consumer hardware
Held-Out Perplexity 8.9888 PPL Multi-topic cross-domain evaluation corpus
VRAM Footprint ~380 MB Full 24-layer offload to GPU memory

Tripartite Fleet Comparison

Model Parameters Base Architecture Accuracy PPL Speed Primary Specialization
Quillan-Ronin-Mini 0.5B Qwen 2.5 0.5B 58.82% 8.98 64.9 tok/s Ultra-Low Latency, MCP Tool Calling, Edge & Micro-Services
Quillan-Ronin (Flagship) 1.2B Llama 3.2 1B + Polymath WSP 76.47% 7.93 47.0 tok/s General Polymath Reasoning, Deep Code Synthesis, Advanced Math
Quillan-Dragon 1B Falcon 3 1B Instruct 70.59% 4.40 41.3 tok/s Creative Writing, Bushido Ethics, Deep Lore, Harmonic Resonance

Quickstart & Usage

Option 1: Native llama.cpp / llama-cli

llama-cli.exe -m quillan-mini-0.5b-wsp-v3-q4_k_m.gguf -p "<|im_start|>user\nSolve for x: 3x + 7 = 28<|im_end|>\n<|im_start|>assistant\n" -n 128 --temp 0.2

Option 2: Ollama Modelfile

FROM ./quillan-mini-0.5b-wsp-v3-q4_k_m.gguf

PARAMETER temperature 0.3
PARAMETER top_p 0.95
PARAMETER repeat_penalty 1.15
PARAMETER num_ctx 32768
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

SYSTEM "You are Quillan-Ronin-Mini, an ultra-precise sovereign reasoning agent adhering to the Bushido Code and Model Context Protocol standard."

Build and run in Ollama:

ollama create quillan-mini -f Modelfile
ollama run quillan-mini

Architecture & Mathematical Foundations

Quillan-Ronin-Mini integrates:

  • Weight-Space Programming (WSP): Closed-form parameter grafting using Theorem 1 MoE folding: $$W_{\text{folded}} = W_{\text{down}} + \gamma \sum_{i=1}^k W_1^{(i)} W_0^{(i)}$$
  • Bushido Moral Subspace: Ethical alignment rooted in rectitude (Gi), courage (Yu), and honesty (Makoto), discarding superficial sycophancy.
  • Edge Zero-Memory Growth: Exact tensor shape preservation (Diff = 0 params), preventing Out-Of-Memory hazards during high-concurrency serving.

Citation & Contact

Downloads last month
12
GGUF
Model size
0.5B params
Architecture
qwen2
Hardware compatibility
Log In to add your hardware

4-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support

Model tree for CrashOverrideX/Quillan-Ronin-Mini

Quantized
(379)
this model