Popular GitHub Repositories for Minimax Alpha Beta Pruning

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

A Collection of Variational Autoencoders (VAE) in PyTorch.

Claude-Flow v2.0.0 Alpha represents a revolutionary leap in AI-powered development orchestration. Built from the ground up with enterprise-grade architecture, advanced swarm intelligence, and seamless Claude Code integration.

TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.

?A curated list of Awesome LLM/VLM Inference Papers with codes: WINT8/4, Flash-Attention, Paged-Attention, Parallelism, etc. ??

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

基于LangChain和ChatGLM-6B等系列LLM的针对本地知识库的自动问答

Sparsity-aware deep learning inference runtime for CPUs

[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.

MiniMax-01 is a simple implementation of the MiniMax algorithm, a widely used strategy for decision-making in two-player turn-based games like Tic-Tac-Toe. The algorithm aims to minimize the maximum possible loss for the player, making it a popular choice for developing AI opponents in various game scenarios.

MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.

A curated list of neural network pruning resources.

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

A curated list for Efficient Large Language Models

javascript gobang AI，JS五子棋AI，源码+教程，基于Alpha-Beta剪枝算法（不是神经网络）

在 oxford hand 数据集上对 YOLOv3 做模型剪枝（network slimming）

Practical course about Large Language Models.