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FlagGems is an operator library for large language models implemented in Triton Language.

Development repository for the Triton language and compiler

An Optimizing Compiler for Recommendation Model Inference

BladeDISC is an end-to-end DynamIc Shape Compiler project for machine learning workloads.

The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.

Open deep learning compiler stack for cpu, gpu and specialized accelerators

Compiler for Dynamic Neural Networks

High-performance In-browser LLM Inference Engine

Chat with AI large language models running natively in your browser. Enjoy private, server-free, seamless AI conversations.

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.

STM32 Application for Deep Learning Framework with TVM

LCAI-TIHU SW is a software stack of the AI inference processor based on RISC-V

Extension package of Apache TVM (Machine Learning Compiler) for Renesas DRP-AI accelerators powered by Edgecortix MERA(TM) Based Apache TVM version: v0.11.1

Tzer: TVM Implementation of "Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation (OOPSLA'22)“.

An extention of TVMScript to write simple and high performance GPU kernels with tensorcore.

An MLIR-based toy DL compiler for TVM Relay.

TVM for Tenstorrent ASICs

Tencent Distribution of TVM

Open deep learning compiler stack for cpu, gpu and specialized accelerators

A polyhedral compiler for expressing fast and portable data parallel algorithms

Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".

[MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Swift for TensorFlow

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gan…