llm-neofetch-plus 1.0.0

Advanced system information tool for local LLM usage

🚀 LLM-Neofetch++

Advanced System Information Tool for Local LLM Usage

Show detailed hardware specs optimized for running local AI models

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✨ Features

🔍 Comprehensive Hardware Detection

• ✅ CPU: Model, cores, threads, frequency, temperature, usage
• ✅ GPU: NVIDIA (nvidia-smi), AMD (rocm-smi), Intel Arc detection
• ✅ VRAM: Total, used, and available video memory
• ✅ RAM: Physical memory and swap information
• ✅ Storage: Disk type (NVMe/SSD/HDD), capacity, speed benchmarks
• ✅ Battery: Charge level, power status, time remaining (laptops)
• ✅ Apple Silicon: M1/M2/M3/M4 detection with unified memory

🎯 Smart AI/LLM Features

• 🤖 Model Recommendations: Personalized suggestions based on your hardware
• 📊 Quantization Guide: GGUF formats explained (Q2_K through Q8_0)
• 🚀 Backend Comparison: Ollama, llama.cpp, vLLM, ExLlamaV2, LM Studio
• ⚡ Performance Estimates: Token/s predictions for different model sizes
• 💡 Optimization Tips: Specific advice for your system configuration

🎨 Beautiful UI

• 🌈 Color-coded Output: Easy to read with semantic colors
• 📊 Progress Bars: Visual representation of usage and capacity
• 🔧 Configurable: Customize colors, emoji, detail level
• 📱 Responsive: Adapts to terminal width

🛠️ Developer Friendly

• 📤 Export Formats: JSON, YAML, Markdown
• 🧪 Unit Tests: Comprehensive test coverage
• 🔌 Modular Design: Easy to extend and customize
• 📝 Type Hints: Full type annotations
• 🐛 Verbose Mode: Detailed logging for debugging

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📦 Installation

From Source (Recommended)

# Clone the repository
git clone https://github.com/HFerrahoglu/llm-neofetch-plus.git
cd llm-neofetch-plus

# Install dependencies
pip install -r requirements.txt

# Run directly
python llm_neofetch.py

# Or install globally
pip install -e .
llm-neofetch

Using pip

pip install llm-neofetch-plus
llm-neofetch

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🎮 Usage

Basic Usage

# Normal output (default)
llm-neofetch

# Minimal output
llm-neofetch -d 1

# Detailed output with all features
llm-neofetch -d 3

# Interactive mode (choose detail level)
llm-neofetch -i

Advanced Usage

# Run disk benchmark (takes ~10 seconds)
llm-neofetch -b

# Export to different formats
llm-neofetch --export report.json      # JSON format
llm-neofetch --export report.yaml      # YAML format
llm-neofetch --export report.md        # Markdown format

# Verbose logging for debugging
llm-neofetch -v

# Custom config file
llm-neofetch --config /path/to/config.yaml

# Combine options
llm-neofetch -d 3 -b --export full_report.json

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📸 Screenshots

Normal Output

╔══════════════════════════════════════════════════════════════════════════╗
║              ⚡ LLM • NEOFETCH ++  ⚡                                   ║
║         Advanced System Info for Local LLM Usage                         ║
║                    v1.0.0 • 2026 Edition                                 ║
╚══════════════════════════════════════════════════════════════════════════╝

────────────────────────────────────────────────────────────────────────────
💻 System Information
────────────────────────────────────────────────────────────────────────────
  OS             Linux-6.5.0-1-amd64-x86_64-with-glibc2.38
  Kernel         6.5.0 (x86_64)
  Uptime         2d 14h 32m
  Python         3.11.5

────────────────────────────────────────────────────────────────────────────
🔧 CPU
────────────────────────────────────────────────────────────────────────────
  Model          AMD Ryzen 9 7950X 16-Core Processor
  Cores          16 physical / 32 threads
  Frequency      4200 MHz
  Usage          [███████████░░░░░░░░░░░░░░░░░░░]  35.2%

────────────────────────────────────────────────────────────────────────────
🎮 GPU
────────────────────────────────────────────────────────────────────────────
    🟢 NVIDIA GeForce RTX 4090
      VRAM: 24.0 GB total
            [████████████░░░░░░░░░░░░░] 12.4/24.0 GB
      Usage          [█████░░░░░░░░░░░░░░░░░░░░] 20.0%
      Temp: 58°C

────────────────────────────────────────────────────────────────────────────
🎯 Personalized Model Recommendations
────────────────────────────────────────────────────────────────────────────

  ▸ Extra Large Models (70-72B)
    • Llama 3.1 70B
    • Qwen2.5 72B

  ▸ Large Models (30-34B)
    • Llama 3.1 33B
    • Qwen2.5 32B

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⚙️ Configuration

LLM-Neofetch++ uses a YAML configuration file. By default, it looks for:

1. ./config/config.yaml (in the project directory)
2. ~/.config/llm-neofetch/config.yaml
3. /etc/llm-neofetch/config.yaml

Sample Configuration

# UI Settings
ui:
  box_width: 76
  use_emoji: true
  show_progress_bars: true
  compact_mode: false

# Color Theme
colors:
  primary: "\033[1;34m"    # Blue
  success: "\033[1;32m"    # Green
  warning: "\033[1;33m"    # Yellow
  danger: "\033[1;31m"     # Red

# Performance Thresholds
thresholds:
  vram:
    excellent: 24  # GB
    good: 12
    moderate: 8

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🔧 Development

Project Structure

llm-neofetch-plus/
├── llm_neofetch.py          # Main application
├── src/
│   ├── detectors.py         # Hardware detection modules
│   └── ui.py                # UI rendering and formatting
├── config/
│   └── config.yaml          # Configuration file
├── tests/
│   └── test_all.py          # Unit tests
├── requirements.txt         # Python dependencies
├── setup.py                 # Package setup
└── README.md               # This file

Running Tests

# Run all tests
python tests/test_all.py

# Run with pytest (if installed)
pytest tests/

# Run with coverage
pytest --cov=src tests/

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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🎯 Use Cases

For AI/ML Developers

• Quickly assess if your hardware can run specific models
• Get token/s estimates before downloading large models
• Understand which quantization format to use
• Optimize your LLM stack configuration

For System Administrators

• Monitor system resources for AI workloads
• Export reports for documentation
• Benchmark storage performance for model loading
• Track GPU utilization and temperatures

For Researchers

• Document hardware specs in papers
• Compare performance across different systems
• Generate reproducible system reports
• Share hardware configurations

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🚀 Roadmap

• Docker container support
• Web dashboard (optional)
• Historical tracking and graphs
• Cloud GPU detection (AWS, GCP, Azure)
• LLM benchmarking suite
• Automatic model download suggestions
• Integration with popular LLM frameworks

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🤝 Acknowledgments

• Built with psutil for cross-platform system info
• Inspired by neofetch
• Community feedback from r/LocalLLaMA

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📄 License

MIT License - see LICENSE file for details

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🌟 Star History

If you find this tool useful, please consider giving it a star ⭐

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

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Made with ❤️ for the Local LLM Community