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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zenlm/zen3-omni")
model = AutoModelForMultimodalLM.from_pretrained("zenlm/zen3-omni", device_map="auto")
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
```

zeekay commited on Feb 28

Commit

573986a

verified ·

1 Parent(s): 33f26ff

Update model card: fix YAML tags, add zen/zenlm branding

Browse files

Files changed (1) hide show

README.md +3 -23

README.md CHANGED Viewed

@@ -6,11 +6,10 @@ tags:
   - multimodal
   - vision
   - audio
-  - omni
   - zen
   - zen3
-  - zenlm
   - hanzo
 license: apache-2.0
 ---
@@ -26,30 +25,11 @@ license: apache-2.0
 | Context Length | 202K tokens |
 | Architecture | Zen MoDE (Mixture of Distilled Experts) |
 | Generation | Zen3 |
-| Modalities | Text, Image, Audio, Video |
-## Usage
-```python
-from transformers import AutoProcessor, AutoModelForCausalLM
-import torch
-processor = AutoProcessor.from_pretrained("zenlm/zen3-omni", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(
-    "zenlm/zen3-omni",
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    trust_remote_code=True,
-)
-messages = [{"role": "user", "content": "Describe this image and transcribe any audio."}]
-inputs = processor.apply_chat_template(messages, return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_new_tokens=512)
-print(processor.decode(outputs[0]))
-```
 ## API Access
 ```python
 from openai import OpenAI

   - multimodal
   - vision
   - audio
   - zen
   - zen3
   - hanzo
+  - zenlm
 license: apache-2.0
 ---
 | 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.
 ```python
 from openai import OpenAI