IntelLabs / Model-Compression-Research-Package
A library for researching neural networks compression and acceleration methods.
☆139Updated 7 months ago
Alternatives and similar repositories for Model-Compression-Research-Package:
Users that are interested in Model-Compression-Research-Package are comparing it to the libraries listed below
- [NeurIPS 2022] A Fast Post-Training Pruning Framework for Transformers☆185Updated 2 years ago
- ☆203Updated 3 years ago
- Prune a model while finetuning or training.☆402Updated 2 years ago
- [ICML'21 Oral] I-BERT: Integer-only BERT Quantization☆242Updated 2 years ago
- Awasome Papers and Resources in Deep Neural Network Pruning with Source Code.☆156Updated 7 months ago
- [CVPR'20] ZeroQ: A Novel Zero Shot Quantization Framework☆277Updated last year
- Pytorch implementation of BRECQ, ICLR 2021☆272Updated 3 years ago
- A research library for pytorch-based neural network pruning, compression, and more.☆160Updated 2 years ago
- The PyTorch implementation of Learned Step size Quantization (LSQ) in ICLR2020 (unofficial)☆131Updated 4 years ago
- [ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization☆94Updated 2 years ago
- PyTorch implementation for the APoT quantization (ICLR 2020)☆271Updated 4 months ago
- Code for the NeurIPS 2022 paper "Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning".☆118Updated last year
- A curated list of Early Exiting papers, benchmarks, and misc.☆112Updated last year
- Block Sparse movement pruning☆79Updated 4 years ago
- ☆43Updated last year
- Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.☆431Updated last year
- Code repo for the paper BiT Robustly Binarized Multi-distilled Transformer☆106Updated last year
- Post-Training Quantization for Vision transformers.☆215Updated 2 years ago
- ☆76Updated 2 years ago
- The official PyTorch implementation of the ICLR2022 paper, QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quan…☆119Updated last year
- This project is the official implementation of our accepted ICLR 2022 paper BiBERT: Accurate Fully Binarized BERT.☆88Updated last year
- In progress.☆63Updated last year
- Code for our paper at ECCV 2020: Post-Training Piecewise Linear Quantization for Deep Neural Networks☆69Updated 3 years ago
- ☆47Updated 3 years ago
- On-the-fly Structured Pruning for PyTorch models. This library implements several attributions metrics and structured pruning utils for n…☆164Updated 4 years ago
- [ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, T…☆29Updated 3 years ago
- Unofficial implementation of LSQ-Net, a neural network quantization framework☆290Updated 11 months ago
- Code repo for the paper "LLM-QAT Data-Free Quantization Aware Training for Large Language Models"☆280Updated last month
- ☆229Updated 2 years ago
- [ICLR 2021] HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark☆110Updated 2 years ago