zenlm

zen-family

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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

Table with columns: Model, Parameters, Active, Use Case, Memory (INT4)
Model	Parameters	Active	Use Case	Memory (INT4)
Zen-Nano	0.6B	0.6B	Edge/Mobile	0.3GB
Zen-Eco	4B	4B	Desktop/Laptop	2GB
Zen-Omni	30B	30B	Server/Cloud	15GB
Zen-Coder

🔧 Quick Start

Using Transformers

python
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
from mlx_lm import load, generate

Using llama.cpp

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

🏆 Benchmarks

Language Models

Table with columns: Model, MMLU, GSM8K, HumanEval, HellaSwag
Model	MMLU	GSM8K	HumanEval	HellaSwag
Zen-Nano	45.2%	28.1%	18.3%	72.1%
Zen-Eco	51.7%	32.4%	22.6%	76.4%
Zen-Omni	68.9%	71.2%	48.5%	85.7%
Zen-Coder

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

📈 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
@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.

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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

Table with columns: Model, Parameters, Active, Use Case, Memory (INT4)
Model	Parameters	Active	Use Case	Memory (INT4)
Zen-Nano	0.6B	0.6B	Edge/Mobile	0.3GB
Zen-Eco	4B	4B	Desktop/Laptop	2GB
Zen-Omni	30B	30B	Server/Cloud	15GB
Zen-Coder

🔧 Quick Start

Using Transformers

python
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
from mlx_lm import load, generate

Using llama.cpp

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

🏆 Benchmarks

Language Models

Table with columns: Model, MMLU, GSM8K, HumanEval, HellaSwag
Model	MMLU	GSM8K	HumanEval	HellaSwag
Zen-Nano	45.2%	28.1%	18.3%	72.1%
Zen-Eco	51.7%	32.4%	22.6%	76.4%
Zen-Omni	68.9%	71.2%	48.5%	85.7%
Zen-Coder

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

📈 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
@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.