OpenLLM 🚀

A high-performance language model implementation in Go, built for production

OpenLLM is an ambitious project to create a fully open-source language model implementation in Go, licensed under the GNU General Public License v3 (GPLv3). Our goal is to provide a transparent, community-driven alternative to proprietary language models.

✨ Highlights

• 🚀 High Performance: Optimized for production with advanced kernel fusion and caching
• 🛠 Developer Friendly: Clean Go implementation with comprehensive documentation
• 📊 Production Ready: Built-in monitoring, metrics, and distributed training
• 🔒 100% Open Source: Complete transparency and community-driven development

🎯 Key Features

Core Capabilities

• Advanced transformer architecture with multi-head attention
• Mixed precision support (FP32, FP16, BF16)
• Efficient tensor operations and modular design
• Comprehensive test coverage

Performance Optimizations

• Q/K/V projection fusion for efficient computation
• Memory-efficient attention implementation
• Advanced kernel fusion techniques
• KV caching with LRU eviction

Training System

• Distributed training via TCP/IP
• Background learning for continuous improvement
• Checkpoint management
• Real-time training from multiple sources

Production Tools

• Built-in monitoring and metrics
• RESTful API endpoints
• Real-time progress tracking
• Performance visualization

🚀 Quick Start

Prerequisites

- Go 1.21+
- Git

Installation

git clone https://github.com/yourusername/openllm.git
cd openllm
go mod tidy

Basic Usage

import "openllm/pkg/model"

config := model.DefaultConfig()
transformer := model.NewTransformer(config)
output, err := transformer.Forward(input)

📈 Development Roadmap

Phase 1: Core Implementation (Current)

• Basic model architecture
• Preprocessing pipeline
• Training loop
• Basic metrics
• Testing interface
• Model serialization
• Basic deployment

Phase 2: Advanced Features

• Distributed training
• Advanced metrics
• Model optimization
• Enhanced testing UI
• Performance monitoring
• Automated testing

Phase 3: Production Readiness

• Model serving
• Monitoring system
• Security features
• Documentation
• CI/CD pipeline
• Performance optimization

Phase 4: Enterprise Features

• Multi-model support
• Advanced analytics
• User management
• API gateway
• Integration tools
• Enterprise support

🤝 Contributing

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

📄 License

This project is licensed under the GNU General Public License v3 (GPLv3) - see the LICENSE file for details.

⭐ Why OpenLLM?

• Open Source: Full transparency and community-driven development
• Production Ready: Built for real-world applications
• High Performance: Optimized for modern hardware
• Easy to Use: Clean API and comprehensive documentation
• Flexible: Adaptable to various use cases

📚 Documentation

• Contributing Guide - How to contribute to the project
• Training Data - Our training data sources and policies
• Development Roadmap - Project development phases

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OpenLLM is maintained by the community.
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