Llama2.c is a minimal inference engine designed to execute transformer-based language models using only standard C code. By implementing neural network forward passes without external dependencies or complex runtime environments, it provides a lightweight execution environment for running pre-trained models.
The project distinguishes itself through a focus on portability and resource efficiency. It utilizes static memory allocation to avoid dynamic heap management and maps model parameter files directly into the process address space to minimize memory overhead. The implementation relies on standard library functions and optimized linear algebra routines to perform matrix multiplication, ensuring the engine can operate across diverse hardware environments.
Beyond inference, the repository includes utilities for training custom tokenizers, allowing users to generate vocabulary files and define tokenization rules from raw text data. This combination of model execution and data preparation tools serves as a resource for studying the fundamental mechanics of transformer architectures and deploying neural networks in environments with limited processing power.
