Llm.c | Awesome Repository

This project is a low-dependency engine designed for training large language models using native C and CUDA. It provides a bare-metal environment for tensor computation, allowing for the execution of neural network operations directly on hardware accelerators without the overhead of high-level software abstractions.

The framework distinguishes itself by implementing manual gradient backpropagation and custom hardware-specific kernels, providing granular control over memory mapping and computational precision. It supports distributed training across multiple graphics processors and compute nodes, utilizing collective communication primitives to scale workloads while maintaining numerical consistency through integrated validation tools.

The library includes a comprehensive suite of utilities for data preparation, model checkpoint management, and performance optimization. It covers essential operations such as attention acceleration, layer normalization, and memory-efficient checkpointing, while providing command-line tools for orchestrating training runs and conducting hyperparameter sweeps.

Features

Large Language Model Training Frameworks - A high-performance framework for training large language models using native C and CUDA kernels.
Bare-Metal Deep Learning Engines - Executes neural network training directly on hardware accelerators using low-level code to eliminate high-level software overhead.
Distributed Training Frameworks - A system for scaling neural network training across multiple graphics processors and compute nodes using collective communication primitives.
Low-Level Neural Network Trainers - Provides a low-dependency toolkit for executing tensor operations and gradient backpropagation directly on hardware accelerators.

Features

Large Language Model Training Frameworks - A high-performance framework for training large language models using native C and CUDA kernels.
Bare-Metal Deep Learning Engines - Executes neural network training directly on hardware accelerators using low-level code to eliminate high-level software overhead.
Distributed Training Frameworks - A system for scaling neural network training across multiple graphics processors and compute nodes using collective communication primitives.
Low-Level Neural Network Trainers - Provides a low-dependency toolkit for executing tensor operations and gradient backpropagation directly on hardware accelerators.