Instructions to use zenlm/zen3-omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenlm/zen3-omni with Transformers:

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

pipe = pipeline("text-generation", model="zenlm/zen3-omni")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForTextToWaveform

processor = AutoProcessor.from_pretrained("zenlm/zen3-omni")
model = AutoModelForTextToWaveform.from_pretrained("zenlm/zen3-omni")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zenlm/zen3-omni with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenlm/zen3-omni"
# 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/zen3-omni",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zenlm/zen3-omni

SGLang

How to use zenlm/zen3-omni 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/zen3-omni" \
    --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/zen3-omni",
		"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/zen3-omni" \
        --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/zen3-omni",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zenlm/zen3-omni with Docker Model Runner:
```
docker model run hf.co/zenlm/zen3-omni
```

Zen3 Omni

Zen LM by Hanzo AI — Multimodal model supporting text, image, audio, and video understanding. 202K context for complex analysis.

Specs

Property	Value
Parameters	1T MoE
Context Length	202K tokens
Architecture	Zen MoDE (Mixture of Distilled Experts)
Generation	Zen3

API Access

This model is served exclusively via the Hanzo AI API. Weight downloads are not available for this model tier.

from openai import OpenAI

client = OpenAI(base_url="https://api.hanzo.ai/v1", api_key="YOUR_KEY")
response = client.chat.completions.create(
    model="zen3-omni",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

Get your API key at console.hanzo.ai — $5 free credit on signup.

License

Apache 2.0

Zen LM is developed by Hanzo AI — Frontier AI infrastructure.

Downloads last month: 17

Safetensors

Model size

35B params

Tensor type

BF16

Collection including zenlm/zen3-omni

Zen3 Generation

Collection

Zen 3 generation — legacy zen-3-*, zen3-*, omni, nano, guard, image, reranker, 3d. • 12 items • Updated 4 days ago