🌲 DeepNarra AI Research & Engineering

🏛️ About DeepNarra

DeepNarra is an independent AI research organization dedicated to developing sovereign, high-efficiency neural architectures designed for extreme reasoning depth, deterministic stability, and high-throughput on-device inference.

DeepNarra unifies breakthroughs across Symplectic Hamiltonian continuous latent spaces, Chunk-wise Gated Linear Attention (GLA) recurrence, DAG-guided speculative leap drafting, and 1.58-bit ternary quantization (TQ1_0). Every architecture is validated for zero numerical degradation, transparent dataset lineage, and open-source compliance under the Apache License 2.0.

📊 The DeepNarra Fleet Matrix

🏛️ Core Architectural Pillars

1. 🍎 Hiraya: Continuous Latent Phase Space (Coconut Deliberation)

Hiraya bypasses the discrete autoregressive token bottleneck by computing reasoning chains directly in a 768-dimensional continuous latent manifold. Deliberation steps evolve according to Symplectic Störmer-Verlet Hamiltonian physics ($\Delta E < 10^{-13}$ energy preservation) and Minkowski Lorentz Hyperboloid geometry, projecting reasoning trajectory states into natural language only when conclusions solidify.

2. 🌲 Narra: Bedrock GLA Recurrence & Locked 128K Context

Narra enforces a strictly bounded 128K token context window (131,072) that operates with linear computational complexity. By leveraging Chunk-wise Gated Linear Attention (GLA) with Lyapunov stability decay bounding, Narra maintains constant memory footprints across sustained million-token reasoning epochs without recurrent state drift.

3. 🦈 Mako: Speculative DAG Leap Engine

Mako eliminates serial generation latency by coupling Multi-Token Prediction (k=4) with high-speed Prompt Lookup Decoding (k=50). A specialized $O(1)$ parallel causal DAG attention verification mask validates candidate draft tokens simultaneously in a single forward pass, unlocking generation rates from 1,000 to over 3,000 tokens per second.

4. 👑 Paham: The Sovereign Sage (Omniversal Synthesis)

Paham unites the continuous latent exploration of Hiraya, the linear recurrent bedrock of Narra, and the speculative speed of Mako. Governed by a Shannon Entropy 3-Gear Elastic Router, Paham dynamically scales its active capacity from Edge Nano (~1.2M) to Deep Reasoning (~4.17M), integrated with Awake Continuum GGUF-QLoRA sidecars ensuring $0.00%$ base model degradation.

📱 Sovereign Edge Deployment (1.58-bit TQ1_0)

All primary edge models are distributed in native 1.58-bit Ternary Quantization (model-TQ1_0.gguf) and serialized PyTorch Safetensors:

• Storage Footprint: 0.81 MB (Paham) to 5.85 MB (Hiraya / Narra / Mako).
• RAM Overhead: < 15 MB total working set memory.
• Target Hardware: Raspberry Pi, consumer laptops, mobile edge devices, and embedded ARM microcontrollers.

⚖️ Governance & Integrity

• Zero Fake Scores Policy: DeepNarra strictly forbids synthetic, estimated, or fabricated benchmark scores. All reported figures represent direct empirical telemetry from Colab runtime environments and safetensors tensor inspections.
• Open-Source Licensing: All model weights, configuration scripts, and architectures are released under the Apache License 2.0.
• Upstream Attribution Compliance: All pretraining and fine-tuning datasets are formally credited under their upstream open-source licenses (FineWeb-Edu, Aya Collection, CodeFeedback-Filtered, and No-Robots).