WEBGEN

ESHMO-AI/WEBGEN

WEBGEN is a text generation model from ESHMO-AI. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.

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Hugging Face

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At a glance

Task: Text Generation
Library: transformers
License: apache-2.0
Model type: gpt_oss
Access: Public
Created: Apr 14, 2026
Updated: Apr 14, 2026
SHA: f6c5cd5b

Try a prompt

Base models

unsloth/gpt-oss-20b-bf16

Model card

From the Hugging Face model README

Example Output

<span class="pill">Open weights</span>
<span class="pill">Web-only bias</span>
<span class="pill">Mobile-first output</span>
<span class="pill">No external JS by default</span>

</div> <div style="display:flex; gap:12px; flex-wrap:wrap; margin-top:18px"> <a class="btn" href="https://huggingface.co/Tesslate/WEBGEN-OSS-20B/resolve/main/README.md">Model card</a> <a class="btn ghost" href="https://tesslate.com">Tesslate</a> <a class="btn ghost" href="https://uigenoutput.tesslate.com">Demos</a> <a class="btn ghost" href="https://discord.gg/EcCpcTv93U">Discord</a> </div> <figure class="screens"> <img alt="Hero sample" src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/G9dpxQKrYJlpnj3Pvw3LV.png"> <img alt="Pricing sample" src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/2GrgB4W5VzPqnpD4FJsA-.png"> <img alt="Features sample" src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/lGvrwLBqeS9IJeKgLrMWO.png"> <img alt="Hero sample" src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/3Kh7TkSuxKctsGOtHGRXJ.png"> <img alt="Pricing sample" src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/KYwUop65wR8WikMaw5upL.png"> <img alt="Features sample" src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/H-c5ORCyMpYmlDN52m3im.png"> </figure> </div> <hr/> <div class="grid grid-2" style="margin-top: 1.5rem"> <div class="card"> <h3>What it is</h3> <p><strong>WEBGEN-OSS-20B</strong> focuses solely on generating production-lean websites. It prefers semantic HTML, sane spacing, and modern component blocks (hero, grids, pricing, FAQ).</p> </div> <div class="card"> <h3>Why</h3> <p>Small enough for local runs and fast iteration, while retaining strong structure/consistency for HTML/CSS/Tailwind output.</p> </div> </div> <hr/>

Quickstart

Transformers

<div class="codeblock"><pre> from transformers import AutoModelForCausalLM, AutoTokenizer import torch

model_id = "Tesslate/WEBGEN-OSS-20B" 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)) </pre></div>

vLLM

<div class="codeblock"><pre> vllm serve Tesslate/WEBGEN-OSS-20B \ --host 0.0.0.0 --port 8000 \ --max-model-len 65536 \ --gpu-memory-utilization 0.92 </pre></div>

sglang

<div class="codeblock"><pre> python -m sglang.launch_server \ --model-path Tesslate/WEBGEN-OSS-20B \ --host 0.0.0.0 --port 5000 \ --mem-fraction-static 0.94 \ --attention-backend flashinfer </pre></div>

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

<hr/>

Inference Settings (suggested)

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

Prompts that work well

<div class="grid grid-2"> <div class="card"> <h3>Starter</h3> <div class="codeblock"><pre> 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. </pre></div> </div> <div class="card"> <h3>Design-strict</h3> <div class="codeblock"><pre> 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. </pre></div> </div> </div> <hr/>

Quantization & VRAM (example)

<table> <thead><tr><th>Format</th><th>Footprint</th><th>Notes</th></tr></thead> <tbody> <tr><td>BF16</td><td>8.05 GB</td><td>Fastest, best fidelity</td></tr> <tr><td>GGUF Q5_K_M</td><td>2.89 GB</td><td>Great quality/size trade-off</td></tr> <tr><td>GGUF Q4_K_M</td><td>2.5 GB</td><td>Smallest comfortable for laptops</td></tr> </tbody> </table> <hr/>

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.

<details> <summary><strong>Limitations</strong></summary> <ul> <li>Accessibility: adds headings/labels but ARIA coverage may need review.</li> <li>JS widgets: kept light unless explicitly requested in prompt.</li> </ul> </details> <details> <summary><strong>Ethical Considerations</strong></summary> <ul> <li>Curate prompts appropriately.</li> <li>When using third-party logos/assets, ensure you have rights or use open sources.</li> </ul> </details> <hr/>

Training Summary (research preview)

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.

<hr/>

Example Outputs

Community

Examples: <a href="https://uigenoutput.tesslate.com">uigenoutput.tesslate.com</a>
Discord: <a href="https://discord.gg/EcCpcTv93U">discord.gg/EcCpcTv93U</a>
Website: <a href="https://tesslate.com">tesslate.com</a>

<blockquote> “Why are good design models so expensive” — Tesslate Team </blockquote> <hr/>

Citation

@misc{tesslate_webgen_oss_preview_2025,
title   = {WEBGEN-OSS-20B: Design-first web generation},
author  = {Tesslate Team},
year    = {2025},
url     = {https://huggingface.co/Tesslate/WEBGEN-OSS-20B}
}

</div>

Task: Text Generation
Library: transformers
Type: gpt_oss
License: apache-2.0
Languages: en
Created: Apr 14, 2026
Updated: Apr 14, 2026