Nanodecoder

Build custom LLM from scratch - A comprehensive framework for developing and training large language models from the ground up.

Key Features

Model Architecture

• Transformer-based architecture implementation
• Support for various model sizes and configurations
• Efficient attention mechanisms and optimization techniques

Training Pipeline

• Distributed training capabilities
• Advanced optimization algorithms
• Memory-efficient training strategies
• Real-time monitoring and logging

Model Evaluation

• Comprehensive evaluation metrics
• Benchmark testing suite
• Performance analysis tools
• Model comparison utilities

Deployment Ready

• Model serving and inference optimization
• API integration capabilities
• Scalable deployment options
• Production-ready configurations

Technology Stack

• Deep Learning: PyTorch, Transformers
• Training: Distributed training, Mixed precision
• Evaluation: Custom metrics, Benchmarking
• Deployment: FastAPI, Docker, Kubernetes

Use Cases

• Research: Experiment with novel architectures
• Education: Learn LLM development from scratch
• Production: Deploy custom models for specific tasks
• Experimentation: Test new training techniques

Getting Started

Visit the GitHub repository to start building your own LLM from scratch.