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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
| Date | Stars |
|---|---|
| 2026-07-24 | 311 |
| 2026-07-25 | 311 |
| 2026-07-28 | 311 |
| 2026-07-30 | 311 |
| 2026-07-31 | 311 |
| 2026-08-06 | 311 |
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# Only Train Once (OTO): Automatic One-Shot DNN Training And Compression Framework
## Note: Repository Migration!
We greatly thank for the support and interest from our community. The **Only-Train-Once** (**OTO**) will be migrated and maintained under Microsoft open-source site. Please check out the new home **[microsoft/only_train_once](https://github.com/microsoft/only_train_once)** and kindly help star :star:, fork, or watch.
To distinguish, the current repository will be renamed as **only_train_once_personal_footprint** to commemorate the past research and development efforts onto this series of works :blush:.
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This repository is the (deprecated) Pytorch implementation of **Only-Train-Once** (**OTO**). OTO is an $\color{LimeGreen}{\textbf{automatic}}$, $\color{LightCoral}{\textbf{architecture}}$ $\color{LightCoral}{\textbf{agnostic}}$ DNN $\color{Orange}{\textbf{training}}$ and $\color{Violet}{\textbf{compression}}$ (via $\color{CornflowerBlue}{\textbf{structure pruning}}$ and $\color{DarkGoldenRod}{\textbf{erasing}}$ operators) framework. By OTO, users could train a general DNN either from scratch or a pretrained checkpoint to achieve both high performance and slimmer architecture simultaneously in the one-shot manner (without fine-tuning).
## Publications
Please find our series of works and [bibtexs](https://github.com/tianyic/only_train_once?tab=readme-ov-file#citation) for kind citations.
- [HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning](https://www.researchgate.net/publication/383984919_HESSO_Towards_Automatic_Efficient_and_User_Friendly_Any_Neural_Network_Training_and_Pruning) preprint.
- [OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators](https://arxiv.org/abs/2312.09411) preprint.
- [LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery](https://huggingface.co/papers/2310.18356) **Hugging face #1 Paper of the day**.
- [An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization](https://openreview.net/pdf?id=KBhSyBBeeO) in **TMLR 2023**.
- [OTOv2: Automatic, Generic, User-Friendly](https://openreview.net/pdf?id=7ynoX1ojPMt) in **ICLR 2023**.
- [Only Train Once (OTO): A One-Shot Neural Network Training And Pruning Framework](https://papers.nips.cc/paper/2021/hash/a376033f78e144f494bfc743c0be3330-Abstract.html) in **NeurIPS 2021**.
In addition, we recommend our following efficient ML works.
- [DREAM: Diffusion Rectification and Estimation-Adaptive Models](https://www.tianyuding.com/projects/DREAM/), efficient diffusion training, in **CVPR 2024**.
- [DISTILLM: Towards Streamlined Distillation for Large Language Models](https://arxiv.org/pdf/2402.03898.pdf), LLM distillation, in **ICML 2024**.
**Note, we will release the report of HESSO optimizer this June.** Thanks for the interest and support from our community.
## Installation
We recommend to run the framExcerpt of 10,963 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:764c1c48daf0f5cb, topic:deep-learning, topic:pytorch, topic:training
matched fp:764c1c48daf0f5cb, topic:model-compression, topic:pruning, desc:pruning