UIGEN-X-8B-GGUF

Mungert/UIGEN-X-8B-GGUF

UIGEN-X-8B-GGUF is a machine learning model from Mungert. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

This model was generated using llama.cpp at commit c82d48ec.

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At a glance

Library: transformers
License: apache-2.0
Access: Public
Created: Jul 21, 2025
Updated: Sep 24, 2025
SHA: 311f243d

Base models

Qwen/Qwen3-8B

Model card

From the Hugging Face model README

UIGEN-X-8B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit c82d48ec.

Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;"> Click here to get info on choosing the right GGUF model format </a>

UIGEN-X-8B Hybrid Reasoning UI Generation Model

image/png

Tesslate's hybrid reasoning UI generation model built on Qwen3-8B architecture. Trained to systematically plan, architect, and implement complete user interfaces across modern development stacks.

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

Model Architecture

UIGEN-X-8B implements hybrid reasoning from the Qwen3 family - combining systematic planning with direct implementation. The model follows a structured thinking process:

Problem Analysis — Understanding requirements and constraints
Architecture Planning — Component structure and technology decisions
Design System Definition — Color schemes, typography, and styling approach
Implementation Strategy — Step-by-step code generation with reasoning

This hybrid approach enables both thoughtful planning and efficient code generation, making it suitable for complex UI development tasks.

Complete Technology Coverage

UIGEN-X-8B supports 26 major categories spanning frameworks and libraries across 7 platforms:

Web Frameworks

React: Next.js, Remix, Gatsby, Create React App, Vite
Vue: Nuxt.js, Quasar, Gridsome
Angular: Angular CLI, Ionic Angular
Svelte: SvelteKit, Astro
Modern: Solid.js, Qwik, Alpine.js
Static: Astro, 11ty, Jekyll, Hugo

Styling Systems

Utility-First: Tailwind CSS, UnoCSS, Windi CSS
CSS-in-JS: Styled Components, Emotion, Stitches
Component Systems: Material-UI, Chakra UI, Mantine
Traditional: Bootstrap, Bulma, Foundation
Design Systems: Carbon Design, IBM Design Language
Framework-Specific: Angular Material, Vuetify, Quasar

UI Component Libraries

React: shadcn/ui, Material-UI, Ant Design, Chakra UI, Mantine, PrimeReact, Headless UI, NextUI, DaisyUI
Vue: Vuetify, PrimeVue, Quasar, Element Plus, Naive UI
Angular: Angular Material, PrimeNG, ng-bootstrap, Clarity Design
Svelte: Svelte Material UI, Carbon Components Svelte
Headless: Radix UI, Reach UI, Ariakit, React Aria

State Management

React: Redux Toolkit, Zustand, Jotai, Valtio, Context API
Vue: Pinia, Vuex, Composables
Angular: NgRx, Akita, Services
Universal: MobX, XState, Recoil

Animation Libraries

React: Framer Motion, React Spring, React Transition Group
Vue: Vue Transition, Vueuse Motion
Universal: GSAP, Lottie, CSS Animations, Web Animations API
Mobile: React Native Reanimated, Expo Animations

Icon Systems

Lucide, Heroicons, Material Icons, Font Awesome, Ant Design Icons, Bootstrap Icons, Ionicons, Tabler Icons, Feather, Phosphor, React Icons, Vue Icons

Platform Support

Web Development

Complete coverage of modern web development from simple HTML/CSS to complex enterprise applications.

Mobile Development

React Native: Expo, CLI, with navigation and state management
Flutter: Cross-platform mobile with Material and Cupertino designs
Ionic: Angular, React, and Vue-based hybrid applications

Desktop Applications

Electron: Cross-platform desktop apps (Slack, VSCode-style)
Tauri: Rust-based lightweight desktop applications
Flutter Desktop: Native desktop performance

Python Applications

Web UI: Streamlit, Gradio, Flask, FastAPI
Desktop GUI: Tkinter, PyQt5/6, Kivy, wxPython, Dear PyGui

Development Tools

Build tools, bundlers, testing frameworks, and development environments.

Programming Language Support

26 Languages and Approaches: JavaScript, TypeScript, Python, Dart, HTML5, CSS3, SCSS, SASS, Less, PostCSS, CSS Modules, Styled Components, JSX, TSX, Vue SFC, Svelte Components, Angular Templates, Tailwind, PHP

Visual Style System

UIGEN-X-8B includes 21 distinct visual style categories that can be applied to any framework:

Modern Design Styles

Glassmorphism: Frosted glass effects with blur and transparency
Neumorphism: Soft, extruded design elements
Material Design: Google's design system principles
Fluent Design: Microsoft's design language

Traditional & Classic

Skeuomorphism: Real-world object representations
Swiss Design: Clean typography and grid systems
Bauhaus: Functional, geometric design principles

Contemporary Trends

Brutalism: Bold, raw, unconventional layouts
Anti-Design: Intentionally imperfect, organic aesthetics
Minimalism: Essential elements only, generous whitespace

Thematic Styles

Cyberpunk: Neon colors, glitch effects, futuristic elements
Dark Mode: High contrast, reduced eye strain
Retro-Futurism: 80s/90s inspired futuristic design
Geocities/90s Web: Nostalgic early web aesthetics

Experimental

Maximalism: Rich, layered, abundant visual elements
Madness/Experimental: Unconventional, boundary-pushing designs
Abstract Shapes: Geometric, non-representational elements

Prompt Structure Guide

Basic Structure

To achieve the best results, use this prompting structure below:

[Action] + [UI Type] + [Framework Stack] + [Specific Features] + [Optional: Style]

Examples

Simple Component:

Create a navigation bar using React + Tailwind CSS with logo, menu items, and mobile hamburger menu

Complex Application:

Build a complete e-commerce dashboard using Next.js + TypeScript + Tailwind CSS + shadcn/ui with:
- Product management (CRUD operations)
- Order tracking with status updates  
- Customer analytics with charts
- Responsive design for mobile/desktop
- Dark mode toggle
Style: Use a clean, modern glassmorphism aesthetic

Framework-Specific:

Design an Angular Material admin panel with:
- Sidenav with expandable menu items
- Data tables with sorting and filtering
- Form validation with reactive forms
- Charts using ng2-charts
- SCSS custom theming

Advanced Prompt Techniques

Multi-Page Applications:

Create a complete SaaS application using Vue 3 + Nuxt 3 + Tailwind CSS + Pinia:

Pages needed:
1. Landing page with hero, features, pricing
2. Dashboard with metrics and quick actions
3. Settings page with user preferences
4. Billing page with subscription management

Include: Navigation between pages, state management, responsive design
Style: Professional, modern with subtle animations

Style Mixing:

Build a portfolio website using Svelte + SvelteKit + Tailwind CSS combining:
- Minimalist layout principles
- Cyberpunk color scheme (neon accents)
- Smooth animations for page transitions
- Typography-driven content sections

Tool Calling & Agentic Usage

UIGEN-X-8B supports function calling for dynamic asset integration and enhanced development workflows.

Image Integration with Unsplash

{
  "type": "function",
  "function": {
    "name": "fetch_unsplash_image",
    "description": "Fetch high-quality images from Unsplash for UI mockups",
    "parameters": {
      "type": "object",
      "properties": {
        "query": {
          "type": "string", 
          "description": "Search term for image (e.g., 'modern office', 'technology', 'nature')"
        },
        "orientation": {
          "type": "string",
          "enum": ["landscape", "portrait", "squarish"],
          "description": "Image orientation"
        },
        "size": {
          "type": "string",
          "enum": ["small", "regular", "full"],
          "description": "Image size"
        }
      },
      "required": ["query"]
    }
  }
}

Content Generation Tools

{
  "type": "function", 
  "function": {
    "name": "generate_content",
    "description": "Generate realistic content for UI components",
    "parameters": {
      "type": "object",
      "properties": {
        "type": {
          "type": "string",
          "enum": ["user_profiles", "product_data", "blog_posts", "testimonials"],
          "description": "Type of content to generate"
        },
        "count": {
          "type": "integer",
          "description": "Number of items to generate"
        },
        "theme": {
          "type": "string", 
          "description": "Content theme or industry"
        }
      },
      "required": ["type", "count"]
    }
  }
}

Complete Agentic Workflow Example

# 1. Plan the application
response = model.chat([
    {"role": "user", "content": "Plan a complete travel booking website using React + Next.js + Tailwind CSS + shadcn/ui"}
], tools=[fetch_unsplash_image, generate_content])

# 2. The model will reason through the requirements and call tools:
# - fetch_unsplash_image(query="travel destinations", orientation="landscape") 
# - generate_content(type="destinations", count=10, theme="popular travel")
# - fetch_unsplash_image(query="hotel rooms", orientation="landscape")

# 3. Generate complete implementation with real assets
final_response = model.chat([
    {"role": "user", "content": "Now implement the complete website with the fetched images and content"}
])

Tool Integration Patterns

Dynamic Asset Loading:

Fetch relevant images during UI generation
Generate realistic content for components
Create cohesive color palettes from images
Optimize assets for web performance

Multi-Step Development:

Plan application architecture
Generate individual components
Integrate components into pages
Apply consistent styling and theming
Test responsive behavior

Content-Aware Design:

Adapt layouts based on content types
Optimize typography for readability
Create responsive image galleries
Generate accessible alt text

Inference Configuration

Optimal Parameters

{
    "temperature": 0.6,         # Balanced creativity and consistency (make it lower if quantized!!!!)
    "top_p": 0.9,               # Nucleus sampling for quality
    "top_k": 40,                # Vocabulary restriction
    "max_tokens": 25000,         # Full component generation
    "repetition_penalty": 1.1,  # Avoid repetitive patterns
}

Use Cases & Applications

Rapid Prototyping

Quick mockups for client presentations
A/B testing different design approaches
Concept validation with interactive prototypes

Production Development

Component library creation
Design system implementation
Template and boilerplate generation

Educational & Learning

Teaching modern web development
Framework comparison and evaluation
Best practices demonstration

Enterprise Solutions

Dashboard and admin panel generation
Internal tool development
Legacy system modernization

Technical Requirements

Hardware

GPU: 8GB+ VRAM recommended (RTX 3080/4070 or equivalent)
RAM: 16GB system memory minimum
Storage: 20GB for model weights and cache

Software

Python: 3.8+ with transformers, torch, unsloth
Node.js: For running generated JavaScript/TypeScript code
Browser: Modern browser for testing generated UIs

Integration

Compatible with HuggingFace transformers
Supports GGML/GGUF quantization
Works with text-generation-webui
API-ready for production deployment

Limitations & Considerations

Token Usage: Reasoning process increases token consumption
Complex Logic: Focuses on UI structure rather than business logic
Real-time Features: Generated code requires backend integration
Testing: Output may need manual testing and refinement
Accessibility: While ARIA-aware, manual a11y testing recommended

Community & Support

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

Join our community to share creations, get help, and contribute to the ecosystem.

Citation

@misc{tesslate_uigen_x_2025,
  title={UIGEN-X-8B: Hybrid Reasoning UI Generation with Qwen3},
  author={Tesslate Team},
  year={2025},
  publisher={Tesslate},
  url={https://huggingface.co/tesslate/UIGEN-X-8B}
}

Built with hybrid reasoning capabilities from Qwen3, UIGEN-X-8B represents a comprehensive approach to AI-driven UI development across the entire modern web development ecosystem.

🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

TurboLLM (GPT-4.1-mini)
HugLLM (Hugginface Open-source models)
TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

Function calling against live network services
How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊

Library: transformers
License: apache-2.0
Languages: en
Created: Jul 21, 2025
Updated: Sep 24, 2025