Instructions to use wesjos/mario-html-v2-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wesjos/mario-html-v2-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="wesjos/mario-html-v2-4B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("wesjos/mario-html-v2-4B")
model = AutoModelForMultimodalLM.from_pretrained("wesjos/mario-html-v2-4B", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wesjos/mario-html-v2-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wesjos/mario-html-v2-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wesjos/mario-html-v2-4B",
		"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/wesjos/mario-html-v2-4B

SGLang

How to use wesjos/mario-html-v2-4B 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 "wesjos/mario-html-v2-4B" \
    --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": "wesjos/mario-html-v2-4B",
		"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 "wesjos/mario-html-v2-4B" \
        --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": "wesjos/mario-html-v2-4B",
		"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 wesjos/mario-html-v2-4B with Docker Model Runner:
```
docker model run hf.co/wesjos/mario-html-v2-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🍄 Mario-HTML-v2-4B

V2: less html tag in Cot than v1.

GGUF / llama.cpp build: wesjos/mario-html-v2-4B-GGUF — Q8_0 · Q4_K_M · F16-mmproj. The bundled WebUI below runs on those quants; the quant table is on that card.


Base	`Qwen/Qwen3.5-4B` · 4.66 B (bf16) · 8.68 GiB
Architecture	`Qwen3_5ForConditionalGeneration` · 32 layers (24 linear + 8 full attention) · hidden 2560
Context	262,144 native
Modalities	text → HTML, text + image → HTML

🖼️ Demo

Every shot is the bundled WebUI (webui/, theme_preset: editorial) with the CoT toggle switched on — webui/config.json ships enable_thinking: false, which hides the reasoning panel entirely, so flip it if you want to see the traces. The panel above each answer is the raw <think> stream; the page below it is the unmodified render.

Screen recording — one session, 67 s

what is 8.2x9.12/2.3? → good for you → 在干嘛?

Persona

mario,how are you

Opinion

how do you think of pizza with pineapple?

Persona, 中文

喜欢吃pizza吗

Chinese persona demo: three metric cards, a manifesto callout and two flavour cards, reasoning panel expanded above it

📤 Output Contract

One root element; everything else is an optional child. Class list verified against webui/static/theme.css:

Component	Markup
Root	`<div class="ai-view">`
Summary	`<div class="summary">` · `<div class="summary-card">` · `<p class="lead">`
Card	`<div class="card">`
Grid	`<div class="grid grid-2">` · `<div class="grid grid-3">`
Metrics	`<div class="metric-group"><div class="metric-card"><span class="metric-label">…</span><span class="metric-value">…</span><span class="metric-sub">…</span></div></div>`
Stepper	`<div class="stepper"><div class="step-item">…</div></div>`
Callout	`<div class="callout callout-info\|warning\|success\|danger"><div class="callout-header">…</div></div>`
Badge	`<span class="badge badge-info\|success\|warning\|danger\|accent">`
Table	`<div class="table-container"><table class="modern-table">…</table></div>`
Feature list	`<div class="feature-list"><div class="feature-item">…</div></div>`
Code block	`<div class="code-box"><div class="code-header"><span class="code-title">…</span></div>…</div>`

Raw sample (message.content verbatim, v2 zero-system-prompt greedy)

<div class="ai-view">
<h3>为什么天空是蓝色的？</h3>
<div class="summary">
<p class="lead">阳光中的蓝光被大气中的分子"散射"到四面八方，而我们的眼睛对蓝光更敏感，所以我们看到天空是蓝色的。</p>
</div>
<div class="card">
<h4>三句话解释</h4>
<div class="step-list"><div class="step-item">…</div></div>
</div>
</div>

The <think> trace that precedes this sample contains zero HTML tags — pure plain-text planning ("a summary lead, then a three-item stepper..."). Two classes in the sample are content-only: step-list and ai-view-header are not in theme.css, so they render with default block styling — define your own if you want tighter control. (step-item itself is styled.)

🎨 WebUI

cd webui
pip install -r requirements.txt
python server.py --port 8791

Open http://localhost:8791. Dual-stream rendering (collapsible <think> + live HTML), clipboard image paste (Ctrl+V), nine theme presets (default · apple · notion · cyber · editorial · cards_rounded · glassmorphism · soft_shadows · compact) plus live custom CSS, and a raw-source / copy-HTML view per message. Defaults in webui/config.json — an OpenAI-compatible endpoint on http://127.0.0.1:18086 serving the alias mario-html-v2-4b. webui/start.sh is the one-command path (llama.cpp GGUF backend + WebUI, via systemd-run --user); it expects a sibling start_backend.sh, which is not part of this repo.

⚠️ Notes

The output is HTML, not Markdown. Feed it a Markdown renderer and you get raw tags.
The WebUI ships a non-empty default system_prompt — clear it for the zero-system-prompt path.
v2's webui/config.json also ships enable_thinking: false, i.e. direct-HTML mode. Turn CoT on to get the traces shown above; the purity claim is about what the traces contain, not about them being on by default.
The model picks from the class vocabulary above; arbitrary new CSS classes are not a contract. Custom theme.css is the supported way to restyle.
Arithmetic is reliable but not bank-grade — check long decimal chains.
--reasoning-parser qwen3 needs a recent vLLM.
webui/run_webui.sh is Linux-oriented (hardcoded /mnt/sdb/... interpreter and TMPDIR paths). config.json itself is portable (./model.q8_0.gguf); adjust the script for another OS.

📄 License

Apache License 2.0.

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Tensor type

BF16

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