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SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
PyTorch native quantization and sparsity for training and inference
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM
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.
Neural Network Compression Framework for enhanced OpenVINO™ inference
Network Slimming (Pytorch) (ICCV 2017)
More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt
[NeurIPS'23] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.
Caffe for Sparse and Low-rank Deep Neural Networks
An innovative library for efficient LLM inference via low-bit quantization
Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".
Sparse Optimisation Research Code
Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).
[CVPR 2021] Exploring Sparsity in Image Super-Resolution for Efficient Inference
Caffe for Sparse Convolutional Neural Network
Sparse and structured neural attention mechanisms
Sparse Inferencing for transformer based LLMs
Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626