---
license: apache-2.0
tags:
- zen
- mlx
- gguf
- safetensors
- zenlm
language:
- en
pipeline_tag: text-generation
---

# Zen Guard Gen v1.0.1

## Available Formats

This model is available in multiple formats for different platforms:

### SafeTensors (Base Format)
- Standard HuggingFace format
- Compatible with Transformers library
- Use for training and fine-tuning

### MLX Format (Apple Silicon Optimized)
- `/mlx/` - Full precision MLX format
- `/mlx-4bit/` - 4-bit quantized (fastest on Mac)

### GGUF Format (Coming Soon)
- Will be added for llama.cpp compatibility
- CPU-optimized for all platforms

## Quick Start

### Using Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("zenlm/zen-guard-gen-8b")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-guard-gen-8b")
```

### Using MLX (Apple Silicon)
```python
from mlx_lm import load, generate

# Load 4-bit model (fastest)
model, tokenizer = load("zenlm/zen-guard-gen-8b", adapter_path="mlx-4bit")

# Generate
response = generate(model, tokenizer, prompt="Your prompt", max_tokens=256)
print(response)
```

### Using llama.cpp (GGUF - Coming Soon)
```bash
llama-cli -m gguf/zen-guard-gen-q4_k_m.gguf -p "Your prompt"
```

## Training with Zoo-Gym

```bash
pip install zoo-gym
zoo-gym train --model zenlm/zen-guard-gen-8b --data your_data.jsonl
```

## Model Details

- **Architecture**: Zen Guard architecture
- **Training**: Zoo-Gym with RAIS (Recursive AI Self-Improvement System)
- **License**: Apache 2.0
- **Partnership**: Hanzo AI x Zoo Labs Foundation

## Citation

```bibtex
@misc{zen_zen_guard_gen_2025,
    title={Zen Guard Gen v1.0.1},
    author={Hanzo AI and Zoo Labs Foundation},
    year={2025},
    version={1.0.1}
}
```