llama.cpp: Writing A Simple C++ Inference Program for GGUF LLM Models

Llama.cpp is a C/C++ framework for inferring machine learning models defined in the GGUF format on multiple execution backends. It was created to infer LLMs from Meta on Apple's silicon, AVX/AVX-512, CUDA, and Arm Neon-based environments. Llama.cpp uses ggml, a low-level framework that provides primitive functions required by deep learning models and abstracts backend implementation details from the user. The project has no dependencies on other third-party libraries, making it lightweight and efficient. This tutorial explores the internals of llama.cpp and creates a basic chat program using low-level functions from llama.cpp. The C++ code written in this tutorial is also used in SmolChat, a native Android application that allows users to interact with LLMs/SLMs in a chat interface on-device. The tutorial covers the program flow, llama.cpp constructs, and creates a simple chat program. The code for this tutorial is available on GitHub. Llama.cpp is different from PyTorch/TensorFlow in that it focuses solely on inference, whereas PyTorch and TensorFlow are end-to-end solutions offering data processing, model training/validation, and efficient inference in one package. By using llama.cpp, developers can create efficient and lightweight inference-only solutions for their machine learning models.