Instructions to use zai-org/UI2Code_N with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/UI2Code_N with Transformers:

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

pipe = pipeline("image-text-to-text", model="zai-org/UI2Code_N")
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("zai-org/UI2Code_N")
model = AutoModelForImageTextToText.from_pretrained("zai-org/UI2Code_N")
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 zai-org/UI2Code_N with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/UI2Code_N"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/UI2Code_N",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/UI2Code_N

SGLang

How to use zai-org/UI2Code_N 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 "zai-org/UI2Code_N" \
    --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": "zai-org/UI2Code_N",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "zai-org/UI2Code_N" \
        --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": "zai-org/UI2Code_N",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use zai-org/UI2Code_N with Docker Model Runner:
```
docker model run hf.co/zai-org/UI2Code_N
```

UI2Code_N

File size: 922 Bytes

f1ac63e

[gMASK]<sop>
{%- for msg in messages %}
    {%- if msg.role == 'system' %}
<|system|>
{{ msg.content }}
    {%- elif msg.role == 'user' %}
<|user|>{{ '\n' }}

        {%- if msg.content is string %}
{{ msg.content }}
        {%- else %}
            {%- for item in msg.content %}
                {%- if item.type == 'video' or 'video' in item %}
<|begin_of_video|><|video|><|end_of_video|>
                {%- elif item.type == 'image' or 'image' in item %}
<|begin_of_image|><|image|><|end_of_image|>
                {%- elif item.type == 'text' %}
{{ item.text }}
                {%- endif %}
            {%- endfor %}
        {%- endif %}
    {%- elif msg.role == 'assistant' %}
        {%- if msg.metadata %}
<|assistant|>{{ msg.metadata }}
{{ msg.content }}
        {%- else %}
<|assistant|>
{{ msg.content }}
        {%- endif %}
    {%- endif %}
{%- endfor %}
{% if add_generation_prompt %}<|assistant|>
{% endif %}