Characterization study repository for model compression method: pruning
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Updated
Jun 2, 2024 - Python
Characterization study repository for model compression method: pruning
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Pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference. This method works for single-layer and multi-layer networks, as well as for restricting focus to a fixed number of communities when desired.
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
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
🤗 Optimum Intel: Accelerate inference with Intel optimization tools
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
Architecture for pruning methods analysis using pytorch prune module
Chess engine
This is the official implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models", and it is also an efficient LLM compression tool with various advanced compression methods, supporting multiple inference backends.
AIMET GitHub pages documentation
PaddleSlim is an open-source library for deep model compression and architecture search.
Code for the paper "FOCIL: Finetune-and-Freeze for Online Class-Incremental Learning by Training Randomly Pruned Sparse Experts"
Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"
Code for CPAL-2024 paper "Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates"
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