microsoft / only_train_onceLinks
OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
☆48Updated last year
Alternatives and similar repositories for only_train_once
Users that are interested in only_train_once are comparing it to the libraries listed below
Sorting:
- [TMLR] Official PyTorch implementation of paper "Efficient Quantization-aware Training with Adaptive Coreset Selection"☆35Updated last year
- ☆13Updated last year
- ☆78Updated 3 years ago
- [ICML 2023] This project is the official implementation of our accepted ICML 2023 paper BiBench: Benchmarking and Analyzing Network Binar…☆56Updated last year
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆34Updated 2 years ago
- [TMLR] Official PyTorch implementation of paper "Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precisio…☆46Updated last year
- [NeurIPS 2024] Search for Efficient LLMs☆15Updated 10 months ago
- [ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Li…☆54Updated last year
- [ECCV 2024] Isomorphic Pruning for Vision Models☆79Updated last year
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆39Updated last year
- ☆30Updated last year
- [ICML 2022] "DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks", by Yonggan …☆72Updated 3 years ago
- Official implementation for ECCV 2022 paper LIMPQ - "Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance"☆62Updated 2 years ago
- Position-based Scaled Gradient for Model Quantization and Pruning Code (NeurIPS 2020)☆25Updated 5 years ago
- In progress.☆66Updated last year
- ☆25Updated 3 years ago
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆30Updated last year
- Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.☆135Updated 3 years ago
- [ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, T…☆32Updated 3 years ago
- ☆22Updated 3 years ago
- [ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization☆94Updated 3 years ago
- ☆36Updated 2 years ago
- This project is the official implementation of our accepted ICLR 2022 paper BiBERT: Accurate Fully Binarized BERT.☆88Updated 2 years ago
- The official implementation of PTQD: Accurate Post-Training Quantization for Diffusion Models☆101Updated last year
- [ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlo…☆77Updated 2 years ago
- [ICLR'21] Neural Pruning via Growing Regularization (PyTorch)☆82Updated 4 years ago
- Page for the CVPR 2023 Tutorial - Efficient Neural Networks: From Algorithm Design to Practical Mobile Deployments☆12Updated 2 years ago
- Code repo for the paper BiT Robustly Binarized Multi-distilled Transformer☆113Updated 2 years ago
- Pytorch implementation of RAPQ, IJCAI 2022☆23Updated 2 years ago
- DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training (ICLR 2023)☆31Updated 2 years ago