#Mojo

Mojo is a new programming language combining Python syntax with systems programming features. It is designed to grow into a Python superset, providing extensive documentation, a standard library, and examples. Users can select between stable releases and nightly builds. The open-source community encourages contributions and discussions.

Basalt utilizes Mojo to offer an innovative machine learning framework constructed from the ground up, delivering performance near that of Rust or C++. Targeted at AI developers, Basalt provides parametric functions and compile-time parameters for improved performance optimization. Initially achieving speeds similar to established frameworks like PyTorch, Basalt undergoes continuous enhancements, presenting a rich feature set including custom Tensor implementations, enhanced benchmarks, and performance profiling. With future plans encompassing GPU support and ONNX compatibility, Basalt is crafted for contributors who seek a robust development base.

This project enhances the Llama2 model inference using Mojo's SIMD and vectorization, offering a 250x speed increase in Python performance. It exceeds llama2.c by 30% and llama.cpp by 20% in multithreaded CPU tasks. Supported models are Stories (260K to 110M) and Tinyllama-1.1B-Chat-v0.2. Benchmarks on Apple M1 Max demonstrate its proficiency. Suitable for developers exploring efficient transformer models in Mojo.

Endia is a scientific computing library that features automatic differentiation and JIT compilation, suited for advanced complex number applications. It offers both imperative and functional interfaces, akin to PyTorch and JAX. While still in development and not production-ready, Endia aims to advance AI and scientific computing with high-performance, relying solely on Mojo and MAX to simplify integration.