Popular GitHub Repositories for Token Pruning

A CLI tool to convert your codebase into a single LLM prompt with source tree, prompt templating, and token counting.

? KIMI AI 长文本大模型逆向API【特长：长文本解读整理】，支持高速流式输出、智能体对话、联网搜索、探索版、K1思考模型、长文档解读、图像解析、多轮对话，零配置部署，多路token支持，自动清理会话痕迹，仅供测试，如需商用请前往官方开放平台。

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.

⚡️ Python client for the unofficial ChatGPT API with auto token regeneration, conversation tracking, proxy support and more.

Sparsity-aware deep learning inference runtime for CPUs

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

? DeepSeek-V3 R1大模型逆向API【特长：良心厂商】（官方贼便宜，建议直接走官方），支持高速流式输出、多轮对话，联网搜索，R1深度思考，零配置部署，多路token支持，仅供测试，如需商用请前往官方开放平台。

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

A curated list for Efficient Large Language Models

Easy token price estimates for 400+ LLMs. TokenOps.

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

Practical course about Large Language Models.

OpenMMLab Model Compression Toolbox and Benchmark.

PaddleSlim is an open-source library for deep model compression and architecture search.

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)