Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation

compiler learning resources collect.

Windows system utilities to maximize productivity

Reinforcement learning tutorials

强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Berkeley's Spatial Array Generator

Unofficial implementation of LSQ-Net, a neural network quantization framework

Demonstrator on running TVM on RISC-V with RN18 and KWS examples

AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for…

AISystem 主要是指AI系统，包括AI芯片、AI编译器、AI推理和训练框架等AI全栈底层技术

The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.

A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (p…

模型压缩的小白入门教程

Awesome list for LLM pruning.

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

Kolmogorov Arnold Networks

A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…

Winograd minimal convolution algorithm generator for convolutional neural networks.

Python bindings for ggml

Tensor library for machine learning

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

A simple and effective LLM pruning approach.

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Anthropic's educational courses

Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚

Tensors and Dynamic neural networks in Python with strong GPU acceleration

🦜🔗 Build context-aware reasoning applications

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

Official inference library for Mistral models

Code for the paper "Language Models are Unsupervised Multitask Learners"