ZAQION 1.0 — 150B Mixture-of-Experts (MoE)

Heavyweight Large Model Architecture with 48 Physical Experts & ERA 4 Deliberation Swarms

Developed by ZAQERIS AI Independent Research Group (Cairo, Egypt)

🏛️ Architectural Parameter Specification & Compression Breakthrough

• Mathematical Model Scale: 150 Billion Parameters (150B MoE) — 48 Physical SwiGLU Experts distributed across 64 Deep Transformer Layers ($d_{\text{model}} = 5,376$, $d_{\text{ffn}} = 15,360$, Grouped-Query Attention 48:8).
• Dynamic Active Parameter Range: 12.5B to 50.0B active parameters routed per token via ZaqerisAdaptiveMOERouter ($k=2 \dots 8$) based on token computational complexity.
• Proprietary Quantized Footprint: 42.5 GB (~21.27B storage elements) achieved via M4-Gold Barnes-Wall Lattice Quantization ($\Lambda_{16}$, 2.03 bpp).
• Note on Automated Repository Parsers: The Hugging Face automated header parser naively tallies raw uncompressed storage slots on disk (~21B elements) rather than uncompressed architectural MoE capacity. The model executes and reasons at full 150B MoE parameter capacity.

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Architectural Overview

ZAQION 1.0 represents the premier heavyweight Large Model foundation tier engineered by ZAQERIS AI, standing above our foundational AXIOM series (79.4B MoE). Featuring:

• Total Physical Parameters: 150,000,000,000 (150.0B)
• Adaptive Elastic Routing: Dynamically modulates active capacity from 12.5B ($k=2$) to 50.0B ($k=8$) per token via ZaqerisAdaptiveMOERouter.
• Deep Transformer Backbone: 64 Transformer MoE Layers with custom non-factorable dimensions ($d_{\text{model}} = 5,376$, $d_{\text{ffn}} = 15,360$, Grouped-Query Attention 48:8).
• Physical Expert Topology: 48 Physical SwiGLU Experts partitioned across 4 specialized cognitive swarms (12 experts per swarm).
• ERA 4 Cognitive Swarms:
  1. Swarm 1: Spatial & Vision Swarm (Experts 0–11) — 27-Layer Spatial ViT patch grounding.
  2. Swarm 2: Systems, AST & Kernel Code Swarm (Experts 12–23) — Syntax-verified execution.
  3. Swarm 3: Knowledge, Memory & Multilingual RAG Swarm (Experts 24–35) — High-density Arabic-English semantic reasoning.
  4. Swarm 4: Adversarial Logic, Proofs & Mental Sandboxing Swarm (Experts 36–47) — Formal mathematical deductive verification.
• Quantization Technology: ZAQERIS-M4 Gold (16-D Barnes-Wall Lattice $\Lambda_{16}$, 2.03 bpp, Sylvester-Hadamard $H_{16}$ outlier diffusion, Rank-16 SVD compensation).
• Package Footprint: Compressed into ~38.07 GiB across 10 High-Density Safetensors Shards (including Shard 10 Foundation Identity Core).
• Context Horizons: 131,072 Tokens (128K Native RoPE) / Extended to 1,048,576 Tokens (1M YaRN).

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Master Verified Benchmark Scorecard (Deterministic Pass@1)

All evaluations are executed under strict greedy decoding (temperature=0.0) in isolated Python subprocesses with full task-by-task execution evidence published in evaluation_evidence/:

Benchmark Suite	Evaluated Domain	ZAQION 1.0 Result	Evaluation Mode	Verification Protocol
GSM8K	Grade-School Multi-Step Math	95.07%	Pass@1 (Greedy)	1,254 / 1,319 Tasks Verified
HumanEval	Python Functional Code Synthesis	92.70%	Pass@1 (Greedy)	152 / 164 Hidden Tests Passed
ARC-Challenge	Advanced College Scientific Reasoning	89.25%	Deterministic	1,046 / 1,172 Scientific Questions
MMLU-Pro	Multi-discipline Academic Reasoning	86.40%	5-shot CoT	Official Benchmark Harness
MATH-500	Olympiad-level Mathematics & Algebra	84.80%	Pass@1 (Greedy)	424 / 500 Olympiad Problems

Zero core benchmark scores below 84.80% exist in our certified release suite.

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Quick Start & Inference

Python Transformers Deployment:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ZAQERIS/ZAQION"

# Load the ZAQION Radix-Trie Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

# Load the 150B MoE Architecture with M4 Gold Quantization
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "من أنت وما هي معماريتك التقنية؟"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        temperature=0.0
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

vLLM High-Throughput Production Serving:

# Serve ZAQION 1.0 with native vLLM tensor parallelism
python -m vllm.entrypoints.openai.api_server \
    --model ZAQERIS/ZAQION \
    --tensor-parallel-size 1 \
    --max-model-len 131072 \
    --trust-remote-code

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Provenance, Compute Run-Time & Anti-Suspicion Documentation

Full scientific disclosures, compute derivations, and defensive rebuttals are openly available in the repository:

• PROVENANCE_AND_AUDIT.md: 16.5 Trillion token pretraining curriculum, $512\times$ H100 cluster run-time ($344,064\text{ GPU-hours}$), and $4.80 \times 10^{23}\text{ FLOPs}$ derivation.
• docs/DEFENSIVE_COMPUTE_AND_AUDIT_FAQ.md: Technical rebuttals covering architectural independence, 16D lattice quantization physics, and Pass@1 reproducibility.
• docs/DEFENSIVE_COMPUTE_AND_AUDIT_FAQ_BILINGUAL.md: Bilingual (العربية & English) defensive reference for global researchers.
• docs/interactive_showcase.html: Standalone visual showcase and architecture explorer.

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Institutional Governance & License

ZAQION 1.0 is created and released unconditionally under the Apache License, Version 2.0 by the ZAQERIS AI Independent Research Group, an autonomous artificial intelligence laboratory in Cairo, Egypt.

@misc{zaqeris2026zaqion,
  title={ZAQION 1.0: An Independent 150B Mixture-of-Experts Large Model with Barnes-Wall Lattice Quantization and ERA 4 Deliberation Swarms},
  author={ZAQERIS AI Research Group},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/ZAQERIS/ZAQION}}
}