Instructions to use wesjos/Spark-x2.6-4B-HTML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wesjos/Spark-x2.6-4B-HTML with Transformers:

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

pipe = pipeline("text-generation", model="wesjos/Spark-x2.6-4B-HTML", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("wesjos/Spark-x2.6-4B-HTML", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wesjos/Spark-x2.6-4B-HTML with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wesjos/Spark-x2.6-4B-HTML"
# 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/Spark-x2.6-4B-HTML",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wesjos/Spark-x2.6-4B-HTML

SGLang

How to use wesjos/Spark-x2.6-4B-HTML 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/Spark-x2.6-4B-HTML" \
    --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/Spark-x2.6-4B-HTML",
		"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 "wesjos/Spark-x2.6-4B-HTML" \
        --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/Spark-x2.6-4B-HTML",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wesjos/Spark-x2.6-4B-HTML with Docker Model Runner:
```
docker model run hf.co/wesjos/Spark-x2.6-4B-HTML
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Spark-X2.6-4B · HTML

Spark-X2.6-4B fine-tuned to answer with rendered HTML — cards, tables, metric tiles and callouts instead of prose.

_{4.11 B params · bf16, 7.66 GiB in one shard · 1 M context · ships a WebUI · Apache-2.0}

💾 Want the same model at 2.4 GB? → Spark-x2.6-4B-HTML-GGUF

The identical fine-tune, quantized: Q4_K_M at 2.42 GiB, Q8_0 at 4.07 GiB. This repo is the full bf16 checkpoint (7.66 GiB) plus the WebUI.

🖼 Demo

Both shots are the bundled WebUI (webui/, theme_preset: notion) with the CoT toggle on — the Chain of Thought panel above each answer is the raw <think> stream, and the page below it is the unmodified render.

`can u show me what ReLU looks like?`

A definition card, a two-column grid with a plotted ReLU curve next to a domain/output/slope table, and a "Why It Matters in ML" bullet card. 30.09 s · 129 tok/s · 3882 tokens.

ReLU demo: a definition card, a two-column grid holding a plotted ReLU curve and a domain-output-slope table, a why-it-matters bullet card, and a footer showing 30.09 seconds, 129 tokens per second and 3882 tokens

_{ReLU · a definition card, a ReLU curve beside a domain/output/slope table, and a “Why It Matters in ML” card}

`how to learn C++`

A study-plan page: a "Why Start Here?" callout, four feature-list bullets, then a "Key Libraries" card with inline code chips.

C++ learning demo: a why-start-here callout, four feature-list bullets for basics, control flow, functions and standard IO, and a key-libraries card with inline code chips for vector, string, map, set, algorithm and numeric

_{C++ · a “Why Start Here?” callout, four feature-list bullets, and a “Key Libraries” card of inline code chips}

📐 Specs


Base	`XHToken/Spark-X2.6-4B` (Apache-2.0)
Architecture	`Spark2_5ForCausalLM` · 36 layers — 9 full-attention + 27 sliding-attention (window 512) · hidden 2560
Attention	16 heads / 4 KV heads · head_dim 256 · partial RoPE 0.25 on full-attention layers
Parameters	4,112,079,360 (bf16, measured from the safetensors header) · 335.5 M embedding / 3.78 B non-embedding
On disk	1 shard, 7.66 GiB
Context	1,048,576 native (`max_position_embeddings`) · tokenizer advertises 131,072
Vocab	131,072
Default system prompt	`You are Mario-html. You are a helpful assistant who generates nice visual content.`

🚀 Quickstart

This repo's tokenizer_config.json declares tokenizer_class: "TokenizersBackend", which needs transformers>=5.0.0. On transformers==4.57.1 (the version upstream pins) AutoTokenizer raises ValueError: Tokenizer class TokenizersBackend does not exist or is not currently imported.

pip install "transformers>=5.0.0" jinja2

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "wesjos/Spark-x2.6-4B-HTML"

tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL, trust_remote_code=True, dtype=torch.bfloat16, device_map="auto"
)

messages = [{"role": "user", "content": "can u show me what ReLU looks like?"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt",
    enable_thinking=True, reasoning_effort="low",
).to(model.device)

out = model.generate(**inputs, max_new_tokens=8192, do_sample=True,
                     temperature=0.7, top_p=0.8, top_k=40, repetition_penalty=1.05)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

llama.cpp — native spark2_5 support landed in b10828; v0.9.0 has it too. The GGUF carries its own chat template, so no --chat-template-file is needed.

llama-cli -m Spark-x2.6-4B-HTML.Q4_K_M.gguf -p "can u show me what ReLU looks like?" -n 8192

🎨 WebUI

The repo ships a self-contained WebUI (webui/) that renders the model's HTML output live, with a collapsible <think> panel, a raw-source view and a copy-HTML button per message, plus five theme presets (default · apple · notion · cyber · editorial).

cd webui
pip install -r requirements.txt
python server.py --port 8798

Open http://localhost:8798. The UI talks to an OpenAI-compatible backend over webui/config.json — by default http://127.0.0.1:18080 serving the alias Spark-x2.6-4B-HTML — and falls back to whatever the backend actually serves if the alias has drifted.

webui/config.json ships model_path: "/mnt/sdb/models/Spark-x2.6-4B-HTML/Spark-x2.6-4B-HTML-Q8_0.gguf" — a path from the training host that does not exist on any reader's machine. Point it at your own quant, or set backend_mode: "direct" and let the UI manage the llama.cpp server for you.

Output contract

Class list verified against the shipped 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>`

💡 Notes

The output is HTML, not Markdown. Render it in a container with theme.css, or the classes fall back to browser defaults.
The persona is in the weights, and so is the system prompt. This repo's chat_template.jinja overrides the upstream default with "You are Mario-html. You are a helpful assistant who generates nice visual content." — unlike the CuteHuman sibling, it is a template edit, not just a weight edit.
Its generation_config.json is also its own: temperature 0.7 / top_p 0.8 / top_k 40 / repetition_penalty 1.05 / max_new_tokens 8192, where upstream ships 1.0 / 0.95 / -1 / 1.0. The WebUI config agrees.
reasoning_effort has three levels here, not two. The WebUI offers 低 / 中 / 高 and the server maps low and medium to low, high and xhigh to high — because the template only understands low and high.

⚠️ Limitations

The screenshots were taken under this repo's WebUI, but the crops shipped here were re-taken from the originals and the thinking dropdown reads 思考: 中（标准推演） — i.e. medium, which the server maps down to reasoning_effort: low.
No training metadata ships with this repo — no data size, no steps, no rank.
Tool use is inherited and unaudited.