Instructions to use zenlm/zen-family with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenlm/zen-family with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zenlm/zen-family")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-family")
model = AutoModelForMultimodalLM.from_pretrained("zenlm/zen-family")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zenlm/zen-family with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenlm/zen-family"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zenlm/zen-family",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zenlm/zen-family

SGLang

How to use zenlm/zen-family with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zenlm/zen-family" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zenlm/zen-family",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zenlm/zen-family" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zenlm/zen-family",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zenlm/zen-family with Docker Model Runner:
```
docker model run hf.co/zenlm/zen-family
```

🌟 Zen AI Model Family v1.0.1

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

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

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)

from mlx_lm import load, generate

Using llama.cpp

# 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

📈 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

@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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Safetensors

Model size

8B params

Tensor type

BF16

Collection including zenlm/zen-family

Zen Meta & Tools

Collection

Meta repos for the Zen family — index, blog, training recipes. • 3 items • Updated 12 days ago