SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
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Updated
May 27, 2024 - Python
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Neural Network Compression Framework for enhanced OpenVINO™ inference
Code for CPAL-2024 paper "Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates"
Code for the paper "FOCIL: Finetune-and-Freeze for Online Class-Incremental Learning by Training Randomly Pruned Sparse Experts"
PaddleSlim is an open-source library for deep model compression and architecture search.
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
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Sparse Optimisation Research Code
Model Compression Made Easy
[NeurIPS'23] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.
Official implementation of the paper "HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization"
Code to reproduce the experiments of the ICLR24-paper: "Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging"
[ICCV2023 Official PyTorch code] for Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
Project examing sparse deep learning architectures for ligand classification.
[AAMAS 2023] Code for the paper "Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning"
[ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective
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