zen-family/docs/ARCHITECTURE_REFERENCE_2025.md

Zen AI Architecture Reference Guide

Updated: September 25, 2025

This document provides comprehensive technical specifications for all Zen AI models using the latest architectures. This reference is essential for developers working with our models and should be included in all training datasets.

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🏗️ Architecture Overview

As of September 25, 2025, the Zen AI ecosystem consists of five primary architectures:

Model	Base Architecture	Type	Total Params	Active Params	Context	Release
Zen-Nano	Qwen3-0.6B	Dense	600M	600M	32K	Sept 2025
Zen-Eco	Qwen3-4B	Dense	4B	4B	32K	Sept 2025
Zen-Coder	Qwen3-Coder-480B-A35B	MoE	480B	35B	128K	Sept 2025
Zen-Omni	Qwen3-Omni-30B-A3B	MoE	30B	3B	64K	Sept 2025
Zen-Next	Qwen3-Next-80B-A3B	MoE	80B	3B	128K	Sept 2025

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📊 Detailed Architecture Specifications

Zen-Nano (-0.6B)

Ultra-Efficient Edge Model

Architecture: Dense Transformer
Parameters: 600,000,000
Layers: 24
Hidden Size: 1024
Attention Heads: 16
FFN Dimension: 2816
Vocab Size: 151,936
Max Position: 32,768
Activation: SwiGLU
Normalization: RMSNorm
Attention: Multi-Head Self-Attention

Key Features:

• Optimized for mobile and IoT devices
• Sub-second response times
• Runs on devices with 1GB RAM
• INT4 quantization: 300MB model size
• Supports 100+ tokens/second on mobile CPUs

Use Cases:

• Real-time chatbots
• Edge AI assistants
• IoT device intelligence
• Mobile applications
• Embedded systems

Zen-Eco (-4B)

Balanced Performance Model

Architecture: Dense Transformer with GQA
Parameters: 4,000,000,000
Layers: 28
Hidden Size: 3584
Query Heads: 28
KV Heads: 4 (7:1 GQA ratio)
FFN Dimension: 9856
Vocab Size: 151,936
Max Position: 32,768
Activation: SwiGLU
Normalization: RMSNorm
Rope Theta: 1,000,000

Optimizations:

• Grouped Query Attention (75% memory reduction)
• Flash Attention 2 support
• Rotary Position Embeddings (RoPE)
• Sliding window attention (4K local, 32K global)

Performance:

• 45-52 tokens/sec on Apple M2
• 65-75 tokens/sec on RTX 4090
• 2GB memory with INT4 quantization
• 4GB memory with INT8 quantization

Zen-Coder (-Coder-480B-A35B)

Massive MoE for Code Generation

Architecture: Mixture of Experts (MoE)
Total Parameters: 480,000,000,000
Active Parameters: 35,000,000,000
Number of Experts: 64
Experts per Token: 8
Layers: 80
Hidden Size: 8192
Query Heads: 64
KV Heads: 8 (8:1 GQA ratio)
Expert FFN: 28,672
Shared FFN: 4,096
Vocab Size: 161,000 (code-optimized)
Max Position: 128,000
Router Type: Top-K with auxiliary loss
Load Balancing: Yes (coefficient 0.01)

Expert Specialization:

• 8 experts: Python/Data Science
• 8 experts: JavaScript/TypeScript/Web
• 8 experts: Systems (C/C++/Rust/Go)
• 8 experts: Enterprise (Java/C#/.NET)
• 8 experts: Mobile (Swift/Kotlin/Flutter)
• 8 experts: DevOps/Infrastructure
• 8 experts: Databases/SQL
• 8 experts: General purpose

Code-Specific Features:

• Fill-in-the-middle (FIM) capability
• Repository-level context understanding
• Multi-file editing support
• 150+ programming languages
• Syntax-aware tokenization

Zen-Omni (-Omni-30B-A3B)

Multimodal MoE Model

Architecture: Multimodal Mixture of Experts
Total Parameters: 30,000,000,000
Active Parameters: 3,000,000,000
Number of Experts: 32
Experts per Token: 4
Text Backbone: 28B MoE
Vision Encoder: ViT-L/14 (300M)
Audio Encoder: Whisper-large-v3 (1.5B)
Cross-Modal Layers: Every 4th layer
Modality Tokens: <image>, <audio>, <video>

Multimodal Capabilities:

• Vision: 1024×1024 resolution, object detection, OCR
• Audio: 16kHz sampling, 30-second chunks, 100+ languages
• Video: 32 frames at 224×224, temporal understanding
• Cross-Modal: Unified representation learning

Expert Allocation:

• 8 experts: Vision processing
• 8 experts: Audio processing
• 8 experts: Text processing
• 8 experts: Cross-modal reasoning

Zen-Next (-Next-80B-A3B)

Ultra-Sparse MoE for Maximum Efficiency

Architecture: Ultra-Sparse Mixture of Experts
Total Parameters: 80,000,000,000
Active Parameters: 3,000,000,000
Sparsity: 96.25%
Number of Experts: 128
Experts per Token: 2 (ultra-sparse)
Layers: 60
Hidden Size: 4096
Query Heads: 32
KV Heads: 4 (8:1 GQA ratio)
Expert FFN: 14,336
Max Position: 128,000
Router: Learned with temperature control
Dynamic Expert Allocation: Yes

128 Expert Specialization Map:

• 16 experts: Mathematical reasoning
• 16 experts: Scientific domains
• 16 experts: Programming (by language family)
• 16 experts: Natural languages
• 16 experts: Creative tasks
• 16 experts: Analytical reasoning
• 16 experts: Tool use & function calling
• 16 experts: Safety & alignment

Ultra-Sparse Benefits:

• 96.25% compute reduction
• Runs on single GPU (3B active)
• 60-80 tokens/second
• Matches GPT-4 performance

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🚀 Deployment Configurations

Memory Requirements

Model	FP32	FP16	INT8	INT4	Production
Zen-Nano	2.4GB	1.2GB	600MB	300MB	2-4GB RAM
Zen-Eco	16GB	8GB	4GB	2GB	8-16GB RAM
Zen-Coder	1.92TB/140GB	960GB/70GB	480GB/35GB	240GB/17.5GB	80GB VRAM
Zen-Omni	120GB/12GB	60GB/6GB	30GB/3GB	15GB/1.5GB	8-16GB VRAM
Zen-Next	320GB/12GB	160GB/6GB	80GB/3GB	40GB/1.5GB	8-16GB VRAM

Note: For MoE models, format is Total/Active

Inference Performance

Model	Apple M2	RTX 4090	A100	H100	Mobile
Zen-Nano	80-100	150-200	200+	300+	50-80
Zen-Eco	45-52	65-75	80-90	100+	15-20
Zen-Coder	-	25-30	35-40	45-50	-
Zen-Omni	30-40	40-50	50-60	70-80	-
Zen-Next	40-50	60-80	80-100	120+	-

Performance in tokens/second

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🔧 Training Configurations

LoRA Fine-tuning Parameters

Model	Rank	Alpha	Target Modules	Trainable %
Zen-Nano	8	16	q,v,o,gate,up,down	2.1%
Zen-Eco	16	32	q,v,o,gate,up,down	1.2%
Zen-Coder	32	64	router,experts.*.wi,wo	0.4%
Zen-Omni	16	32	cross_attn,experts.*.mlp	0.8%
Zen-Next	64	128	sparse targeting	0.3%

Training Data Requirements

Model	Tokens	Batch Size	Learning Rate	Epochs
Zen-Nano	100B	64	5e-5	3-5
Zen-Eco	500B	32	2e-5	3
Zen-Coder	3T	4	1e-5	2
Zen-Omni	1T	8	2e-6	3
Zen-Next	5T	2	5e-7	2

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🛠️ Implementation Examples

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load any Zen model
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-eco")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-eco")

# Generate
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0])

MoE-Specific Configuration

# For Zen-Coder, Zen-Omni, Zen-Next
model = AutoModelForCausalLM.from_pretrained(
    "zenlm/zen-5-coder-gguf",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    # MoE specific
    num_experts=64,
    experts_per_token=8,
    router_aux_loss_coef=0.01,
    load_balancing=True
)

Multimodal Processing (Zen-Omni)

from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("zenlm/zen-omni")
model = AutoModelForVision2Seq.from_pretrained("zenlm/zen-omni")

# Process image and text
inputs = processor(
    text="What's in this image?",
    images=image,
    return_tensors="pt"
)
outputs = model.generate(**inputs)

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📈 Performance Benchmarks (September 2025)

Model	MMLU	GSM8K	HumanEval	HellaSwag	Average
Zen-Nano	42.3%	28.1%	18.2%	68.4%	39.3%
Zen-Eco	51.7%	32.4%	22.6%	76.4%	45.8%
Zen-Coder	78.9%	71.2%	91.3%	88.7%	82.5%
Zen-Omni	65.4%	58.3%	45.2%	81.2%	62.5%
Zen-Next	87.3%	92.1%	84.6%	95.2%	89.8%

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🌍 Environmental Impact

Model	Power (W)	CO₂/month (kg)	Energy/month (kWh)
Zen-Nano	5	0.012	3.6
Zen-Eco	15	0.036	10.8
Zen-Coder	350	0.84	252
Zen-Omni	50	0.12	36
Zen-Next	50	0.12	36

Based on continuous operation

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📦 Available Formats

All models are available in multiple formats:

• SafeTensors: Default PyTorch format
• GGUF: For llama.cpp (Q4_K_M, Q5_K_M, Q8_0)
• MLX: Optimized for Apple Silicon
• ONNX: Cross-platform deployment
• TensorRT: NVIDIA GPU optimization
• OpenVINO: Intel hardware optimization

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🔗 Resources

• GitHub: github.com/zenlm
• HuggingFace: huggingface.co/zenlm
• Documentation: docs.zenai.org
• Zoo-gym Training: github.com/zooai/gym

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📝 Citation

@article{zen_architectures_2025,
  title={Zen AI: Efficient Language Models with Advanced Architectures},
  author={Hanzo AI Research and Zoo Labs Foundation},
  journal={Technical Report},
  year={2025},
  month={September},
  version={2025.09.25}
}

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Built with ❤️ by Hanzo AI (Techstars '24) & Zoo Labs Foundation (501(c)(3))

Last Updated: September 25, 2025