Instructions to use zenlm/zen4-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenlm/zen4-mini with Transformers:

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

pipe = pipeline("text-generation", model="zenlm/zen4-mini")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("zenlm/zen4-mini")
model = AutoModelForImageTextToText.from_pretrained("zenlm/zen4-mini")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
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

vLLM

How to use zenlm/zen4-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenlm/zen4-mini"
# 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/zen4-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zenlm/zen4-mini

SGLang

How to use zenlm/zen4-mini 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/zen4-mini" \
    --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/zen4-mini",
		"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/zen4-mini" \
        --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/zen4-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zenlm/zen4-mini with Docker Model Runner:
```
docker model run hf.co/zenlm/zen4-mini
```

zen4-mini / README.md

zeekay

fix: remove MoDE references (MoDE is zen5 only)

2b89424 verified 3 months ago

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	---
	license: apache-2.0
	language:
	- en
	- zh
	- ja
	- ko
	- fr
	- de
	- es
	- pt
	- ru
	- ar
	tags:
	- zen4
	- zenlm
	- hanzo
	- frontier-ai
	- open-weight
	base_model: Qwen/Qwen3.5-4B
	pipeline_tag: text-generation
	library_name: transformers
	---

	# Zen4 Mini

	Zen4 Mini is a 4B parameter language model from the [Zen4 family](https://zenlm.org) by [Zen LM](https://huggingface.co/zenlm) and [Hanzo AI](https://hanzo.ai).

	Built on open-weight weights with Zen4 Frontier architecture for unrestricted, open-ended AI assistance.

	## Model Details

	\| Property \| Value \|
	\|----------\|-------\|
	\| Parameters \| 4B total, 4B active \|
	\| Architecture \| Zen4 Frontier \|
	\| Context \| 262K tokens \|
	\| License \| APACHE-2.0 \|
	\| Family \| Zen4 \|
	\| Tier \| Small \|
	\| Creator \| Zen LM / Hanzo AI \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("zenlm/zen4-mini", torch_dtype="auto")
	tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4-mini")

	messages = [{"role": "user", "content": "Hello, who are you?"}]
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(text, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=512)
	print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
	```

	## Zen4 Family

	\| Model \| Parameters \| Context \| HuggingFace \|
	\|-------\|-----------\|---------\|-------------\|
	\| Zen4 Nano \| 0.8B \| 262K \| [zenlm/zen4-nano](https://huggingface.co/zenlm/zen4-nano) \|
	\| Zen4 Micro \| 2B \| 262K \| [zenlm/zen4-micro](https://huggingface.co/zenlm/zen4-micro) \|
	\| Zen4 Mini \| 4B \| 262K \| [zenlm/zen4-mini](https://huggingface.co/zenlm/zen4-mini) \|
	\| Zen4 \| 9B \| 262K \| [zenlm/zen4](https://huggingface.co/zenlm/zen4) \|
	\| Zen4 Pro \| 27B \| 262K \| [zenlm/zen4-pro](https://huggingface.co/zenlm/zen4-pro) \|
	\| Zen4 Max \| 35B MoE (3B active) \| 262K \| [zenlm/zen4-max](https://huggingface.co/zenlm/zen4-max) \|
	\| Zen4 Coder Flash \| 31B MoE (3B active) \| 131K \| [zenlm/zen4-coder-flash](https://huggingface.co/zenlm/zen4-coder-flash) \|
	\| Zen4 Pro Max \| 80B MoE (3B active) \| 256K \| [zenlm/zen4-pro-max](https://huggingface.co/zenlm/zen4-pro-max) \|
	\| Zen4 Coder \| 80B MoE (3B active) \| 256K \| [zenlm/zen4-coder](https://huggingface.co/zenlm/zen4-coder) \|
	\| Zen4 Mega \| 122B MoE (10B active) \| 262K \| [zenlm/zen4-mega](https://huggingface.co/zenlm/zen4-mega) \|
	\| Zen4 Thunder \| 230B MoE (10B active) \| 1M \| [zenlm/zen4-thunder](https://huggingface.co/zenlm/zen4-thunder) \|
	\| Zen4 Storm \| 456B MoE (45B active) \| 1M \| [zenlm/zen4-storm](https://huggingface.co/zenlm/zen4-storm) \|
	\| Zen4 Titan \| 744B MoE (40B active) \| 128K \| [zenlm/zen4-titan](https://huggingface.co/zenlm/zen4-titan) \|
	\| Zen4 Ultra \| 1.04T MoE (32B active) \| 256K \| [zenlm/zen4-ultra](https://huggingface.co/zenlm/zen4-ultra) \|
	\| Zen4 Ultra Max \| 1T MoE (50B active) \| 128K \| [zenlm/zen4-ultra-max](https://huggingface.co/zenlm/zen4-ultra-max) \|

	## Links

	- [Zen LM](https://zenlm.org) \| [Hanzo AI](https://hanzo.ai) \| [Hanzo Chat](https://hanzo.chat)
	- [All Zen Models](https://huggingface.co/zenlm)

	---

	Zen AI: Clarity Through Intelligence