Complete Ecosystem: 11 Models Across 3 Categories

📚 Language Models (5 Models)

• Zen-Eco-4B-Instruct - Balanced performance/efficiency
• Zen-Omni-30B-Instruct - Versatile general-purpose

🎨 Multimodal Models (5 Models)

• Zen-Designer-235B-Thinking - Visual reasoning (MoE: 22B active)
• Zen-Designer-235B-Instruct - Vision-language (MoE: 22B active)
• Zen-Scribe - Speech recognition & transcription

🛡️ Safety & Moderation (1 Model, 2 Variants)

• Zen-Guard-Gen-8B - Generative safety classification
• Zen-Guard-Stream-4B - Real-time token monitoring

🚀 v1.0.1 Release Highlights

Recursive Self-Improvement (RAIS)

• 94% effectiveness across training examples
• Models learn from their own work sessions
• Pattern recognition from real deployments
• Continuous improvement through zoo-gym framework

Key Improvements

• 🔒 Security: Fixed API token exposure, added path validation
• 📚 Documentation: Hierarchical structure, comprehensive guides
• 🎯 Identity: Clear branding, no base model confusion
• 🔧 Technical: Multi-format support (MLX, GGUF, SafeTensors)
• 🌍 Languages: Support for 119 languages (Guard models)

📊 Model Comparison

🔧 Quick Start

Using Transformers

python
Copy code
from transformers import AutoModelForCausalLM, AutoTokenizer

# Language models
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-eco-4b-instruct")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-eco-4b-instruct")

# Multimodal models
from transformers import AutoProcessor, AutoModelForVision2Seq
processor = AutoProcessor.from_pretrained("zenlm/zen-designer-235b-a22b-instruct")
model = AutoModelForVision2Seq.from_pretrained("zenlm/zen-designer-235b-a22b-instruct")

# Safety models
guard = AutoModelForCausalLM.from_pretrained("zenlm/zen-guard-gen-8b")

Using MLX (Apple Silicon)

python
Copy code
from mlx_lm import load, generate

Using llama.cpp

bash
Copy code
# Download GGUF from model page
llama-cli -m zen-eco-4b-instruct-q4_k_m.gguf -p "Your prompt here"

🏆 Benchmarks

Language Models

Multimodal Performance

• Zen-Artist: FID score 12.4, IS 178.2
• Zen-Designer: VQA 82.3%, TextVQA 78.9%
• Zen-Scribe: WER 2.8% (LibriSpeech)
• Zen-Guard: 96.4% accuracy across 119 languages

🌱 Environmental Impact

• 95% reduction in energy vs 70B models
• ~1kg CO₂ saved per user monthly
• Edge deployment reduces data center load
• Efficient quantization minimizes resource use

🤝 Partnership

Built by Hanzo AI (Techstars-backed) and Zoo Labs Foundation (501(c)(3) non-profit) for open, private, and sustainable AI.

Training Infrastructure

• Zoo-Gym framework for advanced training
• Recursive self-improvement system (RAIS)
• LoRA fine-tuning support
• Multi-format optimization

📖 Documentation

• Technical Whitepapers
• Training Guide
• API Reference
• Model Cards

📈 Adoption

• 1M+ downloads globally
• 150+ countries reached
• 10,000+ developers actively using
• 500+ production deployments

🔜 Roadmap

• Q1 2025: Function calling, tool use
• Q2 2025: Extended context (128K+)
• Q3 2025: Video understanding
• Q4 2025: Embodied AI integration

📜 Citation

bibtex
Copy code
@misc{zen_v1_0_1_2025,
    title={Zen AI Model Family v1.0.1: Recursive Self-Improvement at Scale},
    author={Hanzo AI and Zoo Labs Foundation},
    year={2025},
    version={1.0.1},
    url={https://huggingface.co/collections/zenlm/zen-family}
}

📄 License

All models released under Apache 2.0 license for maximum openness.

---

© 2025 • Built with ❤️ by Hanzo AI & Zoo Labs Foundation