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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-family")
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-family", device_map="auto")
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
```

Superseded — archived checkpoint. This is the v1.0.1 family overview; its weights are a Qwen3-8B fine-tune. See the current lineup at huggingface.co/zenlm. Weights are kept available for reproducibility.

Zen AI Model Family (v1.0.1, archived)

Base model & attribution

Fine-tuned from Qwen/Qwen3-8B (Apache-2.0) with Hanzo identity + agentic-data training + abliteration.

Zen fine-tunes the best open-weight model of each era. Across this family that is the Qwen3 line from Alibaba Cloud — base, VL, Omni, Embedding, Reranker, TTS, ASR, Guard, Coder — with media models on permissive bases (Wan, FLUX, TRELLIS, YuE). Hanzo adds only identity training, agentic-data fine-tuning, and abliteration. There is no from-scratch Zen model.

Upstream model: Qwen/Qwen3-8B
Upstream project: Qwen (Alibaba Cloud)
Upstream license: Apache-2.0

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("zenlm/zen-family")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-family")

messages = [{"role": "user", "content": "Hi, what can you help me with?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Models

Browse the current, canonical lineup at huggingface.co/zenlm.

Citation

@misc{zen_family_2025,
    title  = {Zen AI Model Family},
    author = {Hanzo AI and Zoo Labs Foundation},
    year   = {2025},
    url    = {https://huggingface.co/zenlm}
}