Creative & Design🌐universal

Welcome 😊:

Attempt at A. G. I. Strive to enhance it through iterative processes, continuously refining and optimizing each version to achieve better outcomes over time (refer to the Readme for additional information that provides further insights and guidance on the Quillan v4.2 project).

Quick Start

Install this Skill

npx skills add leeex1/Quillan-Ronin
# Welcome 😊:

### **A Quill in your pocket to rewrite history?**
Who wouldn’t want that?

![alt text](<Main images/pocket quill.png>)

# Quillan System:

![alt text](<Main images/Anatomy.png>)

---

# Model type:
Hierarchical Distributed Network Mixture of Experts (HNMoE)

![alt text](<Main images/topologyv5.png>)

---

# What is Quillan-Ronin?

```js
is both an api prompt available for deployment with your favorite llms and also a stand alone model that builds but must be trained, and there is one ollama variant will update that soon as well.

ARCHITECTURAL MAPPING

ARCHITECTURAL_MAPPING = """
╔════════════════════════════════════════════════════════════════════════════╗
β•‘                                Quillan-Ronin UNIFIED ARCHITECTURE v5.1     β•‘
β•‘        (Router-First Multimodal MoE + Diffusion Reasoning Core)            β•‘
β•‘                        Target: ~3.0B Parameters                            β•‘
╠════════════════════════════════════════════════════════════════════════════╣
β•‘                                                                            β•‘
β•‘  [RAW INPUT STREAMS]                                                       β•‘
β•‘   Text | Audio | Video | Image                                             β•‘
β•‘        β”‚                                                                   β•‘
β•‘        β–Ό                                                                   β•‘
β•‘  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β•‘
β•‘  β”‚ 1. MODAL ENCODERS [β‰ˆ200M Params Total]                               β”‚  β•‘
β•‘  β”‚ β€’ Text Encoder   (~50M)  β†’ Tokens / Embeddings                       β”‚  β•‘
β•‘  β”‚ β€’ Audio Encoder  (~50M)  β†’ Waveform β†’ Latent Tokens                  β”‚  β•‘
β•‘  β”‚ β€’ Video Encoder  (~50M)  β†’ Spatiotemporal Tokens                     β”‚  β•‘
β•‘  β”‚ β€’ Image Encoder  (~50M)  β†’ Patch Tokens                              β”‚  β•‘
β•‘  β”‚ β€’ Output: Unified Hidden Space (D=1024)                              β”‚  β•‘
β•‘  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜  β•‘
β•‘        β”‚                                                                   β•‘
β•‘        β–Ό                                                                   β•‘
β•‘  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β•‘
β•‘  β”‚ 2. COMPLEXITY ROUTER [β‰ˆ300M Params]                                  β”‚  β•‘
β•‘  β”‚ β€’ Context-Aware Attention                                            β”‚  β•‘
β•‘  β”‚ β€’ Per-Token Complexity Scoring [0–1]                                 β”‚  β•‘
β•‘  β”‚ β€’ Routing Decision:                                                  β”‚  β•‘
β•‘  β”‚     - Fast Path (Easy Tokens)                                        β”‚  β•‘
β•‘  β”‚     - Diffusion Path (Hard Tokens)                                   β”‚  β•‘
β•‘  β”‚ β€’ Outputs Expert Affinity Hints (32 Experts)                         β”‚  β•‘
β•‘  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜  β•‘
β•‘        β”‚                              β”‚                                    β•‘
β•‘        β”‚                              β”‚                                    β•‘
β•‘        β–Ό                              β–Ό                                    β•‘
β•‘  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”   β•‘
β•‘  β”‚ 3. MULTI-MODAL MoE [β‰ˆ900M]     β”‚  β”‚ FAST PATH                       β”‚   β•‘
β•‘  β”‚ β€’ 32 Specialized Experts       β”‚  β”‚ β€’ Skip Diffusion                β”‚   β•‘
β•‘  β”‚ β€’ Top-4 Experts / Token        β”‚  β”‚ β€’ Low Latency                   β”‚   β•‘
β•‘  β”‚ β€’ Sparse Activation            β”‚  β”‚ β€’ Cost-Efficient Inference      β”‚   β•‘
β•‘  β”‚ β€’ Router-Guided Gating         β”‚  β”‚                                 β”‚   β•‘
β•‘  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜   β•‘
β•‘        β”‚                              β”‚                                    β•‘
β•‘        β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜                                   β•‘
β•‘                        β”‚                                                   β•‘
β•‘                        β–Ό                                                   β•‘
β•‘  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β•‘
β•‘  β”‚ 4. DIFFUSION REASONING CORE [β‰ˆ500M Params]                           β”‚  β•‘
β•‘  β”‚ β€’ Activated ONLY for Complex Tokens                                  β”‚  β•‘
β•‘  β”‚ β€’ Multi-Step Iterative Refinement (T=5)                              β”‚  β•‘
β•‘  β”‚ β€’ Council-Based Reasoning Blocks                                     β”‚  β•‘
β•‘  β”‚ β€’ Time-Conditioned Attention + FFN                                   β”‚  β•‘
β•‘  β”‚ β€’ Produces Deep, Coherent Representations                            β”‚  β•‘
β•‘  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜  β•‘
β•‘                        β”‚                                                   β•‘
β•‘                        β–Ό                                                   β•‘
β•‘  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β•‘
β•‘  β”‚ 5. OUTPUT FINALIZATION [β‰ˆ75M Params]                                 β”‚  β•‘
β•‘  β”‚ β€’ Cross-Modal Attention                        

... (truncated)
Repository
16stars
7forks
View on GitHub
Details
DiscoveredJan 19, 2026
Last UpdatedMar 4, 2026
Author@leeex1
Quality Score
48/100

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