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A toolkit for subnetwork analysis

Project description

NeuroSurgeon

NeuroSurgeon is a python toolkit built to enable deep learning researchers to easily uncover and manipulate subnetworks within trained models. NeuroSurgeon provides a simple API to inject differentiable binary masks techniques into linear, attention, and convolution layers in BERT, GPT, ResNet, and ViT-style models within Huggingface Transformers. Differentiable masking has a variety of use cases for deep learning research, such as:

Documentation

Read the NeuroSurgeon Documentation

Tutorial

To get started with NeuroSurgeon, check out the tutorial here. This covers the basic workflow for using NeuroSurgeon to uncover functional subnetworks within a trained model.

Install

NeuroSurgeon requires python 3.9 or higher and several libraries, including Transformers and PyTorch. Installation can be done using PyPi:

pip install NeuroSurgeon

Logo Prompt

NeuroSurgeon's logo was created with the help of DALL-E 2, using the prompt "A cute cartoon robot doctor smiling with a stethoscope".

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