Video-to-Video
Cosmos
Safetensors
object-permanence
world-model
video-prediction
physical-reasoning
cosmos3
Instructions to use Hokin/PWM-WROP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use Hokin/PWM-WROP with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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Download README.md from Hokin/PWM-WROP: direct link, hf CLI and curl.
- Browser
- Download file 2.06 kB
-
https://huggingface.co/Hokin/PWM-WROP/resolve/main/README.md
- Command line
-
hf download hf://Hokin/PWM-WROP/README.md
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curl -L -o README.md https://huggingface.co/Hokin/PWM-WROP/resolve/main/README.md
2.06 kB
| license: cc-by-nc-4.0 | |
| base_model: nvidia/Cosmos3-Nano | |
| datasets: | |
| - Hokin/object-permanence | |
| pipeline_tag: video-to-video | |
| tags: | |
| - object-permanence | |
| - world-model | |
| - video-prediction | |
| - physical-reasoning | |
| - cosmos | |
| - cosmos3 | |
| # PWM-WROP | |
| A 16B multimodal world model post-trained for **object permanence** in video generation and reasoning. | |
| ## License and attribution | |
| The fine-tuned weights in this repository are released under | |
| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — attribution: **Hokin Deng**. | |
| Non-commercial use only. For commercial licensing contact | |
| hokinxqdeng@gmail.com. | |
| **Built on NVIDIA Cosmos.** PWM-WROP is a derivative of the base model nvidia/Cosmos3-Nano | |
| (Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES), which NVIDIA distributes under the | |
| OpenMDW License Agreement 1.1. As that agreement requires, a copy is included here as | |
| [LICENSE-OpenMDW-1.1.txt](LICENSE-OpenMDW-1.1.txt) and NVIDIA's copyright notice is retained; its | |
| terms apply to the base-model portions of these weights, and the CC BY-NC 4.0 terms above apply | |
| to the fine-tuning as released. | |
| ## Citation | |
| Paper: *Training Object Permanence in World Models* — [arXiv:2609.28654](https://arxiv.org/abs/2609.28654). | |
| ```bibtex | |
| @misc{zhang2026trainingobjectpermanenceworld, | |
| title = {Training Object Permanence in World Models}, | |
| author = {Haotian Zhang and Fengyuan Yu and Dezhi Luo and Haoran Sun and Zehong Zhao and Qingying Gao and Yihan Li and Siyuan An and Huayi Qin and Yilan Zhang and Zhengze Jiang and Pinyuan Feng and Renrui Zhang and Ziyu Guo and Letian Wang and Mengyue Yang and Kangfu Mei and Maijunxian Wang and Ran Ji and Vikash Kumar and Freda Shi and Chandra Sripada and Vincent C. Muller and Philip Torr and Alan Yuille and Nikolaus Kriegeskorte and Felix Juefei-Xu and Lvmin Zhang and Jieneng Chen and Yilun Du and Hokin Deng}, | |
| year = {2026}, | |
| eprint = {2609.28654}, | |
| archivePrefix = {arXiv}, | |
| primaryClass = {cs.AI}, | |
| url = {https://arxiv.org/abs/2609.28654} | |
| } | |
| ``` | |