Llama Home

A comprehensive training and inference pipeline for LLM models, featuring efficient resource management, advanced monitoring, and enterprise-grade security features.

🌟 Features

• 🚀 High-Performance Training

  • Distributed training with efficient resource utilization
  • Dynamic batch sizing and gradient accumulation
  • Mixed-precision training with automatic optimization
  • H2O integration for enhanced performance

• 📊 Advanced Monitoring

  • Real-time metrics tracking and visualization
  • Custom metric definitions and aggregations
  • Performance profiling and bottleneck detection
  • Resource utilization monitoring

• 💾 Smart Resource Management

  • Multi-tier caching system with configurable policies
  • Memory-efficient data processing with streaming
  • Automatic resource scaling and optimization
  • Checkpoint management with safetensors support

• 🔒 Enterprise Security

  • Role-based access control
  • Secure token management
  • Automated security scanning
  • Comprehensive audit logging

• 🛠️ Developer Experience

  • Clean, modular architecture
  • Comprehensive documentation
  • Extensive test coverage
  • CI/CD integration

🚀 Quick Start

Prerequisites

• Python 3.11+
• CUDA 11.7+ (for GPU support)
• 16GB+ RAM
• 50GB+ storage

Installation

1. Clone the repository:

   git clone https://github.com/zachshallbetter/LlamaHome.git
   cd LlamaHome

2. Set up your environment:

   # Create and activate virtual environment
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   
   # Install dependencies
   pip install -r requirements.txt

3. Configure your environment:

   cp .env.example .env
   # Edit .env with your settings

📚 Documentation

Our comprehensive documentation covers everything you need:

Getting Started

• 🏗️ Architecture Overview
• ⚙️ Setup Guide
• 🔧 Configuration Guide

Core Functionality

• 🎯 Training Guide
• 🔮 Inference Guide
• 📡 API Reference

Advanced Topics

• ⚡ Performance Tuning
• 🔒 Security Guide
• 🐛 Troubleshooting

💻 Example Usage

from llamahome.training import TrainingPipeline, CheckpointManager, MonitorManager
from llamahome.config import Config

# Initialize configuration
config = Config.from_env()

# Set up training components
pipeline = TrainingPipeline(config)
checkpoint_mgr = CheckpointManager(config)
monitor = MonitorManager(config)

# Configure training
pipeline.configure(
    model_name="llama3.3",
    batch_size=32,
    gradient_accumulation=4,
    mixed_precision="fp16"
)

# Train model
results = pipeline.train(
    train_dataset=train_data,
    val_dataset=val_data,
    epochs=10,
    checkpoint_manager=checkpoint_mgr,
    monitor=monitor
)

# Save and analyze results
checkpoint_mgr.save_best(results)
monitor.generate_report()

🧪 Testing

We maintain comprehensive test coverage:

# Run all tests
make test

# Run specific test suites
make test-unit
make test-integration
make test-performance

# Run with coverage report
make test-coverage

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Run tests (make test)
4. Commit your changes (git commit -m 'feat: add amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with PyTorch and Transformers
• Inspired by best practices in ML engineering
• Thanks to all our contributors