Build your own ChatGPT from scratch in C++ (github.com)

A new project, Torchless, has emerged as a custom-built inference engine for large language models (LLMs), developed entirely in C++. It currently supports the Mistral 7B model, enabling local text completion on CPU. The primary objective of Torchless is to optimize inference speed while maintaining compatibility with the latest Mistral model architectures. The project involves comprehensive components like a model loader, tensor utilities, and a tokenizer, offering a fully functional pipeline that transforms user prompts into tokenized inputs for processing. This development is significant for the AI/ML community as it emphasizes high-performance, low-level programming for LLM inference, showcasing how a more efficient architecture can enhance speed, especially for CPU-based systems. Torchless utilizes advanced techniques such as rotary positional embeddings and grouped query attention, designed to improve the model's memory handling and reduce computational overhead. With ongoing efforts to refine performance through SIMD instructions and CUDA optimizations, Torchless represents a promising direction for future LLM implementations that prioritize both efficiency and accessibility for developers.