Lists (6)
Starred repositories
Multilingual Document Layout Parsing in a Single Vision-Language Model
CUDA Tile IR is an MLIR-based intermediate representation and compiler infrastructure for CUDA kernel optimization, focusing on tile-based computation patterns and optimizations targeting NVIDIA te…
A compact implementation of SGLang, designed to demystify the complexities of modern LLM serving systems.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and mo…
TexTeller can convert image to latex formulas (image2latex, latex OCR) with higher accuracy and exhibits superior generalization ability, enabling it to cover most usage scenarios.
cuTile is a programming model for writing parallel kernels for NVIDIA GPUs
Helpful kernel tutorials and examples for tile-based GPU programming
🚀 Local-first collaboration SDK for React, Vue, and Svelte. Batteries-included: Rich text, undo/redo, cursors, and presence.
A comprehensive Rust sandbox implementation that provides process isolation
🦭 Distributed log streaming engine built from first principles
This repo contains the Hugging Face Deep Reinforcement Learning Course.
A collection of TikZ diagrams of neural networks and deep learning concepts for academic use.
Render LaTeX and TikZ diagrams in your notes
Introduction to Machine Learning Systems
TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels
Rust library to read, manipulate and write PDF files.
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
Puzzles for learning Triton, play it with minimal environment configuration!
Lets make video diffusion practical!
depyf is a tool to help you understand and adapt to PyTorch compiler torch.compile.
Flowistry is an IDE plugin for Rust that helps you focus on relevant code.
A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.