LLM inference in C/C++

Slurm: A Highly Scalable Workload Manager

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

[ECCV 2024 Oral] LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation.

a small build system with a focus on speed

[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation

Blazing fast Neovim framework providing solid defaults and a beautiful UI, enhancing your neovim experience.

InstantID: Zero-shot Identity-Preserving Generation in Seconds 🔥

Project documentation with Markdown.

Build smaller, faster, and more secure desktop and mobile applications with a web frontend.

🦜🔗 Build context-aware reasoning applications

OpenGL Mathematics (GLM)

[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering

A collaboration friendly studio for NeRFs

CUDA accelerated rasterization of gaussian splatting

Minimalist ML framework for Rust

A refreshingly simple data-driven game engine built in Rust

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Fish shell like syntax highlighting for Zsh.

Fish-like autosuggestions for zsh

A Zsh theme

🙃 A delightful community-driven (with 2,400+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python…

Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.

Vim-fork focused on extensibility and usability

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

A modern formatting library

An extremely fast Python linter and code formatter, written in Rust.

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.