WEBGEN-4B-Preview

Name: WEBGEN-4B-Preview
Availability: InStock

A 4B web-only generator that turns one prompt into clean, responsive HTML/CSS/Tailwind. Small enough for laptops; opinionated for consistent, modern layouts.

TRY IT HERE! Get on Designer Open weights Web-only bias Mobile-first output No external JS by default

Model card Tesslate Demos Discord

What it is

WEBGEN-4B-Preview focuses solely on generating production-lean websites. It prefers semantic HTML, sane spacing, and modern component blocks (hero, grids, pricing, FAQ).

Why 4B

Small enough for local runs and fast iteration, while retaining strong structure/consistency for HTML/CSS/Tailwind output.

Quickstart

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Tesslate/WEBGEN-4B-Preview"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
prompt = """Make a single-file landing page for 'LatticeDB'.
Style: modern, generous whitespace, Tailwind, rounded-xl, soft gradients.
Sections: navbar, hero (headline + 2 CTAs), features grid, pricing (3 tiers),
FAQ accordion, footer. Constraints: semantic HTML, no external JS."""
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2000, temperature=0.7, top_p=0.9)
print(tok.decode(out[0], skip_special_tokens=True))

vLLM

vllm serve Tesslate/WEBGEN-4B-Preview \
  --host 0.0.0.0 --port 8000 \
  --max-model-len 65536 \
  --gpu-memory-utilization 0.92

sglang

python -m sglang.launch_server \
  --model-path Tesslate/WEBGEN-4B-Preview \
  --host 0.0.0.0 --port 5000 \
  --mem-fraction-static 0.94 \
  --attention-backend flashinfer \
  --served-model-name webgen-4b

Tip: Lower temperature (e.g., 0.4–0.6) yields stricter, cleaner markup. Raise it for more visual variety.

Inference Settings (suggested)

Param	Value	Notes
`temperature`	0.6	Balance creativity & consistency (lower if quantized)
`top_p`	0.9	Nucleus sampling
`top_k`	40	Optional vocab restriction
`max_new_tokens`	1200–2500	Single-file sites often fit < 1500
`repetition_penalty`	1.1	Reduces repetitive classes/markup

Prompts that work well

Starter

Make a single-file landing page for "RasterFlow" (GPU video pipeline).
Style: modern tech, muted palette, Tailwind, rounded-xl, subtle gradients.
Sections: navbar, hero (big headline + 2 CTAs), logos row, features (3x cards),
code block (copyable), pricing (3 tiers), FAQ accordion, footer.
Constraints: semantic HTML, no external JS. Return ONLY the HTML code.

Design-strict

Use an 8pt spacing system. Palette: slate with indigo accents.
Typography scale: 14/16/18/24/36/56. Max width: 1200px.
Avoid shadows > md; prefer borders/dividers.

Quantization & VRAM (example)

Format	Footprint	Notes
BF16	8.05 GB	Fastest, best fidelity
GGUF Q5_K_M	2.89 GB	Great quality/size trade-off
GGUF Q4_K_M	2.5 GB	Smallest comfortable for laptops

Intended Use & Scope

Primary: Generate complete, single-file websites (landing pages, marketing pages, simple docs) with semantic HTML and Tailwind classes.
Secondary: Component blocks (hero, pricing, FAQ) for manual composition.

Limitations

Accessibility: adds headings/labels but ARIA coverage may need review.
JS widgets: kept light unless explicitly requested in prompt.

Ethical Considerations

Curate prompts appropriately.
When using third-party logos/assets, ensure you have rights or use open sources.

Training Summary (research preview)

Base: Qwen/Qwen3-4B-Instruct
Objective: Tight web-only bias; reward semantic structure, spacing rhythm, and responsiveness.
Data: Mixture of curated HTML/CSS/Tailwind snippets, component libraries, and synthetic page specs.
Recipe: SFT with format constraints → instruction tuning → style/rhythm preference optimization.
Context: effective ~64k; trained to keep default outputs within practical page length.

Example Outputs

Community

Examples: uigenoutput.tesslate.com
Discord: discord.gg/EcCpcTv93U
Website: tesslate.com

“Why are good design models so expensive” — Tesslate Team

Citation

@misc{tesslate_webgen_4b_preview_2025,
title   = {WEBGEN-4B-Preview: Design-first web generation with a 4B model},
author  = {Tesslate Team},
year    = {2025},
url     = {https://huggingface.co/Tesslate/WEBGEN-4B-Preview}
}