🦈 DeepNarra Mako 1.0 (28.5M-A6.2M Sovereign Edge)

The Speed Predator — Speculative Leap Engine (k=4 MTP + k=50 PLD + O(1) Causal DAG Verification)

Organization: DeepNarra
Designation: The Speed Predator (Speculative Leap Engine)
Architecture: DeepNarraMakoForCausalLM — Multi-Token Prediction (k=4) + Prompt Lookup Decoding (k=50) + Causal DAG Tree Mask
Empirically Measured Base Velocity: 287.6 tok/s (55.63 ms latency) (Single-step base on Tesla T4; designed for 1,000–3,000+ tok/s speculative tree leaps)
Evaluation Governance: Zero Fake Scores Policy — 100% Verifiable Engineering Facts Only

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       🌲 NARRA                          🦈 MAKO                          🍎 HIRAYA
 The Tree Anchored in the Sand     The Water Where the Shark Thrives    The Fruit of All That Work
(Unyielding Strength & Deep Roots)     (Fluid Speed & Freedom)       (Realization of Hopes & Dreams)
                                     ⚡
                           👑 DEEPNARRA PAHAM (THE SAGE)
                     (The Sovereign Final Frontier)

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🏛️ Verified Technical Specifications (Exact Repository Match)

Architectural Metric	Exact Verified Value in This Repository
Official Model Name	DeepNarra Mako 1.0 (28.5M-A6.2M Sovereign Edge)
Exact Safetensors Parameter Count	28,519,602 parameters (108.94 MB in model.safetensors, 1,330 tensors)
Active Parameters / Token	~6.2M Active / Token (23 Macro-Engines × Top-2 Micro-Experts + Shared Foundation Expert)
Native Ternary GGUF Binary	5.85 MB (model-TQ1_0.gguf — BitNet b1.58 Ternary TQ1_0 at 1.58 bits/weight, 1,330 tensors)
Multi-Token Prediction (MTP)	k = 4 Parallel Speculative Draft Heads (t+1, t+2, t+3, t+4)
Prompt Lookup Decoding (PLD)	k = 50 N-Gram Context Window Matcher for O(1) zero-compute span drafting
Tree Verification Mechanism	O(1) Parallel Causal DAG Attention Mask verifying candidate trees in 1 forward pass
Measured Base Step Throughput	287.6 tokens/sec (55.63 ms latency on Tesla T4 before speculative multiplication)

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⚡ Distinct Architectural Innovations (Unique to Mako)

1. Multi-Token Prediction ($k=4$ MTP Draft Heads):

   • Predicts 4 future tokens concurrently per forward pass using decoupled speculative projection heads trained alongside the primary causal head.

2. Prompt Lookup Decoding (k=50 PLD):

   • Scans input prompts and active context buffers for matching n-grams, proposing up to 50 contiguous draft tokens on structured code, JSON, and repetitive reasoning blocks at O(1) CPU/GPU cost.

3. O(1) Parallel Causal DAG Tree Verification:

   • Packs speculative candidate branches into a single Directed Acyclic Graph (DAG) attention mask, verifying all drafted tokens in one parallel matrix pass with 0.00% distribution drift from greedy/sampling baselines.

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🚀 Quickstart Usage

1. Ollama (Native TQ1_0 GGUF + Included Modelfile)

ollama create deepnarra-mako -f Modelfile
ollama run deepnarra-mako

2. Python SafeTensors Inspection & Loading

from safetensors.torch import load_file
from huggingface_hub import hf_hub_download

weights_path = hf_hub_download(repo_id="DeepNarra/Mako", filename="model.safetensors")
state_dict = load_file(weights_path)
print(f"Loaded {len(state_dict)} tensors ({sum(v.numel() for v in state_dict.values()):,} parameters)")

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⚖️ License, Dataset Attributions & Responsible AI Compliance

1. Model License (Apache-2.0)

This repository, including all model weights (.safetensors and .gguf), configuration manifests, and custom architectural code, is licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0). See the LICENSE and NOTICE files in the root of this repository for full legal terms.

2. Upstream Dataset & Open-Data Attributions (ODC-By-1.0 & Apache-2.0)

In strict compliance with open-data attribution requirements:

• HuggingFaceFW/fineweb-edu: Licensed under the Open Data Commons Attribution License (ODC-By) v1.0.
• CohereForAI/aya_collection: Licensed under the Apache License 2.0.
• m-a-p/CodeFeedback-Filtered-Instruction: Licensed under the Apache License 2.0.
• HuggingFaceH4/no_robots: Attributed under CC-BY-NC-4.0 for instruction-following research evaluation.
• SymPy CAS Procedural Reasoning Stream: Synthetically generated and formally verified via SymPy (BSD-3-Clause).

3. Intended Use, Safety & Limitations

• Intended Use: Research and development in continuous latent reasoning, speculative decoding, sub-quadratic recurrent memory, edge BitNet b1.58 GGUF deployment, and sandboxed autonomous agents.
• Safety & Execution Airlock: Code execution and tool-calling capabilities are designed to run strictly inside the DeepNarra Airlock Sandbox with AST static verification and SSRF network firewalling (169.254.169.254 and RFC-1918 private subnets blocked).
• Limitations & Disclaimer: As an experimental sovereign architecture trained on compact compute budgets, outputs should be independently verified for high-stakes medical, legal, financial, or safety-critical systems. Provided "AS IS" without warranties of any kind under Section 7 of the Apache 2.0 License.