---
license: apache-2.0
tags:
  - zen
  - llm
  - multimodal
  - safety
  - v1.0.1
  - recursive-learning
  - zenlm
  - hanzo
datasets:
  - zooai/gym
language:
  - en
  - zh
  - multilingual
library_name: transformers
pipeline_tag: text-generation
---

# 🌟 Zen AI Model Family v1.0.1

## Complete Ecosystem: 11 Models Across 3 Categories

### 📚 Language Models (5 Models)
- **[Zen-Eco-4B-Instruct](https://huggingface.co/zenlm/zen-eco-4b-instruct)** - Balanced performance/efficiency
- **[Zen-Omni-30B-Instruct](https://huggingface.co/zenlm/zen-omni-30b-instruct)** - Versatile general-purpose

### 🎨 Multimodal Models (5 Models)
- **[Zen-Designer-235B-Thinking](https://huggingface.co/zenlm/zen-designer-235b-a22b-thinking)** - Visual reasoning (MoE: 22B active)
- **[Zen-Designer-235B-Instruct](https://huggingface.co/zenlm/zen-designer-235b-a22b-instruct)** - Vision-language (MoE: 22B active)
- **[Zen-Scribe](https://huggingface.co/zenlm/zen-scribe)** - Speech recognition & transcription

### 🛡️ Safety & Moderation (1 Model, 2 Variants)
- **[Zen-Guard-Gen-8B](https://huggingface.co/zenlm/zen-guard-gen-8b)** - Generative safety classification
- **[Zen-Guard-Stream-4B](https://huggingface.co/zenlm/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

| 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 | 480B | 30B | Code Generation | 15GB |
| Zen-Next | 80B | 80B | Advanced Tasks | 40GB |
| Zen-Artist | 7B | 7B | Image Generation | 3.5GB |
| Zen-Artist-Edit | 7B | 7B | Image Editing | 3.5GB |
| Zen-Designer-Think | 235B | 22B | Visual Reasoning | 11GB |
| Zen-Designer-Inst | 235B | 22B | Vision-Language | 11GB |
| Zen-Scribe | 2B | 2B | Speech-to-Text | 1GB |
| Zen-Guard-Gen | 8B | 8B | Safety Generation | 4GB |
| Zen-Guard-Stream | 4B | 4B | Real-time Safety | 2GB |

## 🔧 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
| 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 | 71.4% | 82.7% | 78.9% | 87.2% |
| Zen-Next | 73.8% | 86.3% | 52.1% | 88.9% |

### 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](https://github.com/zenlm/zen/tree/main/docs/papers/pdfs)
- [Training Guide](https://github.com/zooai/gym)
- [API Reference](https://docs.zenlm.ai)
- [Model Cards](https://huggingface.co/collections/zenlm)

## 📈 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.

---

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