Instructions to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with Ollama:
```
ollama run hf.co/gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M
```
Unsloth Desktop

How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with Docker Model Runner:
```
docker model run hf.co/gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M
```

Lemonade

How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "gabriellarson/UIGEN-T3.1-8B-Full-Demo-No-Reasoning-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

# UIGEN-X-8B — Streamlined UI Generation

Tesslate's optimized UI model, built for direct and efficient code generation. Optimized for popular web develpment stacks such as:

React Ecosystem

React + Tailwind CSS: shadcn/ui, NextUI, DaisyUI, Headless UI, Flowbite React, Tremor, Park UI, Catalyst
React Standalone: Material-UI (MUI), Ant Design (AntD), Chakra UI, Mantine, PrimeReact
Meta Frameworks: Next.js, Remix, Vite, Create React App

Vue.js Ecosystem

Component Libraries: Vuetify, PrimeVue, Tailwind CSS direct integration
Meta Framework: Nuxt.js
Styling: Material Design, comprehensive component suites

Angular Enterprise

Libraries: Angular Material, PrimeNG, ng-bootstrap
Styling: SCSS, CSS, Bootstrap integration
Framework: Angular CLI with enterprise-grade state management

Supported Frameworks: React, Vue.js, Angular, Svelte, Vanilla JS, and more

Styling Engines: Tailwind CSS, Material-UI, Emotion, Bootstrap, SCSS, CSS-in-JS

Component Libraries: 50+ including shadcn/ui, NextUI, Vuetify, Angular Material

Demos

Explore New UI generations: 📂 https://uigenoutput.tesslate.com

Join our Discord: https://discord.gg/GNbWAeJ4 Our Website: https://tesslate.com

Quick Information

UI generation model built on Qwen3 architecture
No-reasoning variant for faster, direct code generation
Supports both components and full web pages
116 web tech stacks supported across React, Vue, Angular, Svelte, and more
Most popular stacks: React+Tailwind (27 variants), Material-UI ecosystem, Vue+Nuxt
Streamlined output: Pure HTML and Tailwind CSS without reasoning overhead
Powered by UIGenEval, a first-of-its-kind benchmark for UI generation
Released under Apache 2.0 license for research and commercial use

Model Details

Base Model: unsloth/Qwen3-8B-unsloth-bnb-4bit
Reasoning Style: No reasoning (direct generation)
Tokenizer: Qwen default, with design token headers
Output: Components + Full pages (with <html>, <head>)
Images: User-supplied or placehold.co – no images in the dataset due to licensing concerns
License: Apache 2.0 (permissive commercial use)

Architecture System

UIGEN-T3.1-8B-Full-Demo-No-Reasoning was trained using a streamlined architecture optimized for direct code generation.

Unlike hybrid reasoning variants, this model focuses on:

Direct code generation → Fast, efficient HTML/CSS output without reasoning blocks
Consistent formatting → Standardized structure for production-ready code
Optimized inference → Reduced latency compared to reasoning-enabled models

Outputs include design tokens at the top of each generation for easier site-wide customization.

Inference Parameters

For optimal performance with this no-reasoning variant:

Parameter	Value
Temperature	0.6
Top P	0.95
Top K	20
Max Tokens	40k+

Evaluation: UIGenEval Framework

UIGenEval is our internal evaluation suite, designed to bridge the gap between creative output and quality assurance. (Learn more in our upcoming paper: "UIGenEval: Bridging the Evaluation Gap in AI-Driven UI Generation" - August, 2025)

UIGenEval evaluates models across four pillars:

Technical Quality — Clean HTML, CSS structure, semantic accuracy
Prompt Adherence — Feature completeness and fidelity to instructions
Interaction Behavior — Dynamic logic hooks and functional interactivity
Responsive Design — Multi-viewport performance via Lighthouse, Axe-core, and custom scripts

This comprehensive framework directly informs our GRPO reward functions for the next release.

Example Prompts to Try

Design a multi-vendor marketplace homepage with live product carousels, seller spotlight cards, and a dynamic filters sidebar using React + Next.js + Tailwind CSS + shadcn/ui. Include loading skeletons and dark mode support.
Generate a subscription management dashboard for a SaaS product (with plan comparisons, usage analytics, billing history, and upgrade modal) in Vue + Nuxt.js + Vuetify. The dashboard should support both admin and user views.
Build an enterprise-grade analytics dashboard with filterable data tables, export buttons, real-time chart widgets (Tremor + Recharts), and user management features. Use React + Next.js + Tailwind CSS + Tremor. Add role-based sidebar navigation and mobile responsiveness.
Create a healthcare metrics dashboard with multi-level drill-down, interactive graphs, and secure login using Angular + Angular Material + SCSS. Support light/dark themes and modular cards.
Design a customizable headless CMS editor panel (fields: text, images, rich embeds, SEO settings) in SvelteKit + Tailwind CSS + Skeleton UI. Embed drag-and-drop section reordering and inline editing.

Use Cases

Use Case	Description
Rapid Prototyping	Quick UI mockups without reasoning delays
Production Templates	Clean, ready-to-use HTML/Tailwind components
Batch Generation	Efficient processing of multiple UI requests
API Integration	Streamlined for REST API and automated workflows
Educational Tools	Teaching HTML/CSS without complex reasoning explanations

Limitations

No reasoning explanations — Pure code output without design rationale
Limited complex logic — Best suited for UI structure, not interactive features

Technical Requirements

GPU: ≥8GB VRAM for 8B inference
Libraries: transformers, torch, unsloth
Python: 3.8+
Compatible with Hugging Face inference APIs and local generation pipelines

Community & Contribution

Join our Discord: https://discord.gg/GNbWAeJ4
Chat about AI, design, or model training
Want to contribute UIs or feedback? Let's talk!

Citation

@misc{tesslate_UIGEN-X,
  title={UIGEN-X: Efficient UI Generation model built on Qwen3},
  author={Tesslate Team},
  year={2025},
  publisher={Tesslate},
  note={Non-commercial Research License},
  url={https://huggingface.co/tesslate/UIGEN-T3}
}

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GGUF

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8B params

Architecture

qwen3

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