sparseml
Model optimizer
Enables the creation of smaller neural network models through efficient pruning and quantization techniques
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
2k stars
49 watching
148 forks
Language: Python
last commit: about 2 years agoLinked from 1 awesome list
automlcomputer-vision-algorithmsdeep-learning-algorithmsdeep-learning-librarydeep-learning-modelsimage-classificationkerasnlpobject-detectiononnxpruningpruning-algorithmspytorchsmaller-modelssparsificationsparsification-recipessparsitytensorflowtransfer-learning
Related projects:
| Repository | Description | Stars |
|---|---|---|
| Tools and techniques for optimizing large language models on various frameworks and hardware platforms. | 2,257 | |
| A collection of unconstrained optimization algorithms for sparse modeling in MATLAB | 53 | |
| Automates machine learning model creation and optimization for complex datasets | 1,857 | |
| Automates the search for optimal neural network configurations in deep learning applications | 468 | |
| Automates model building and deployment process by optimizing hyperparameters and compressing models for edge computing. | 200 | |
| A library of optimized GPU kernels for sparse matrix operations used in deep learning. | 248 | |
| A deep learning method for optimizing convolutional neural networks by reducing computational cost while improving regularization and inference efficiency. | 18 | |
| A tool for optimizing deep learning models to reduce memory usage without sacrificing performance | 308 | |
| A Python package for gradient-based function optimization in machine learning | 181 | |
| Automated machine learning with tree search optimization | 16 | |
| A collection of pre-trained natural language processing models | 170 | |
| Deep learning models for semantic segmentation of images | 101 | |
| A lightweight wrapper around PyTorch to prevent CUDA out-of-memory errors and optimize model execution | 1,823 | |
| Optimization code for topology optimization problems using machine learning and deep learning techniques | 107 | |
| Reimplementation of a neural network model for conditional segmentation of ambiguous images | 548 |