A professional, premium web application for discovering and browsing GGUF (GPT-Generated Unified Format) machine learning models. This platform provides an elegant interface to explore thousands of quantized AI models with detailed information, engagement metrics, and direct download links.
- Model Discovery: Browse 5,000+ GGUF format AI models
- Advanced Search: Real-time search with fuzzy matching
- Smart Filtering: Filter by quantization type, model type, and license
- Engagement Metrics: Like counts, download statistics, and popularity indicators
- Responsive Design: Premium mobile-first responsive interface
- Performance Optimized: Fast loading with efficient data handling
- Professional Design: Business-class styling with premium aesthetics
- Interactive Elements: Smooth animations and hover effects
- Accessibility: WCAG compliant with keyboard navigation support
- Dark Mode: Automatic dark mode support based on user preferences
- Mobile Optimized: Collapsible header and mobile-friendly interactions
- SEO Optimized: Structured data, meta tags, and prerendering support
- GitHub Integration: Automated workflows for data updates
- Modular Architecture: Component-based JavaScript architecture
- Performance Monitoring: Built-in analytics and performance tracking
├── index.html # Main application entry point
├── css/
│ ├── premium-styles.css # Main premium styling
│ └── *.css # Component-specific styles
├── js/
│ ├── premium-app.js # Main application controller
│ ├── components/ # Reusable UI components
│ ├── services/ # Data and business logic services
│ ├── state/ # Application state management
│ └── utils/ # Utility functions and helpers
└── scripts/ # Build and automation scripts
- Data Fetching: Python scripts fetch model data from Hugging Face
- Processing: Data is processed and enriched with engagement metrics
- Storage: JSON files store processed model information
- Rendering: JavaScript dynamically renders the UI
- Interaction: User interactions update filters and views
- Node.js (v16 or higher)
- Python 3.8+
- Git
# Clone the repository
git clone https://github.com/local-ai-zone/local-ai-zone.github.io.git
cd gguf-model-discovery
# Install Python dependencies
pip install -r scripts/requirements.txt
# Start local development server
python -m http.server 8000
# Open in browser
open http://localhost:8000# Install development dependencies
npm install
# Run data fetching script
python scripts/simplified_gguf_fetcher.py
# Start development server with live reload
npm run dev{
"modelName": "string",
"description": "string",
"quantization": "string",
"fileSize": "number",
"downloadCount": "number",
"likeCount": "number",
"license": "string",
"modelType": "string",
"downloadUrl": "string"
}- Primary: Hugging Face Hub API
- Enrichment: Community engagement metrics
- Updates: Automated daily refresh via GitHub Actions
- Color Palette: Professional blue and neutral tones
- Typography: Inter font family for modern readability
- Spacing: Consistent 8px grid system
- Components: Reusable design tokens and components
- CSS Variables: Centralized theming system
- BEM Methodology: Block-Element-Modifier naming convention
- Responsive Design: Mobile-first approach with breakpoints
- Performance: Optimized CSS with minimal unused styles
# Optional: API rate limiting
HUGGINGFACE_TOKEN=your_token_here
# Optional: Analytics
ANALYTICS_ID=your_analytics_id- Prerendering: Static page generation for SEO
- Minification: CSS and JS optimization
- Compression: Gzip compression for assets
# Build for production
npm run build
# Deploy to GitHub Pages
npm run deploy- Build the project:
npm run build - Upload
dist/folder to your web server - Configure server for SPA routing (if needed)
test-*.html # Integration tests
verify-*.js # Unit tests
*-test.html # Component tests
# Run all tests
npm test
# Run specific test suite
npm run test:engagement
npm run test:filters
npm run test:mobile- Lazy Loading: Images and components loaded on demand
- Virtual Scrolling: Efficient rendering of large model lists
- Caching: Intelligent caching of API responses
- Compression: Optimized asset delivery
- First Contentful Paint: < 1.5s
- Largest Contentful Paint: < 2.5s
- Cumulative Layout Shift: < 0.1
- First Input Delay: < 100ms
- Content Security Policy: Strict CSP headers
- HTTPS Only: Secure connections required
- Input Sanitization: XSS prevention
- Rate Limiting: API abuse prevention
- No Personal Data: No user data collection
- External Links: Clear disclaimer about third-party content
- Transparency: Open source and auditable code
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Make your changes and test thoroughly
- Commit with conventional commits:
git commit -m "feat: add amazing feature" - Push to your branch:
git push origin feature/amazing-feature - Open a Pull Request
- JavaScript: ES6+ with modern syntax
- CSS: BEM methodology with CSS variables
- HTML: Semantic markup with accessibility
- Testing: Comprehensive test coverage
This project is licensed under the MIT License - see the LICENSE file for details.
- Hugging Face: For providing the model data and API
- GGUF Loader: For inspiration and branding partnership
- Community: For feedback and contributions
- Built with Kiro: This project leverages Kiro for AI-assisted development, including code generation and workflow automation.
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: support@gguf-discovery.com
Disclaimer: GGUF Loader is not affiliated with Hugging Face. All links point to publicly available models hosted by their respective creators. We do not store or redistribute any model files directly.