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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.
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
| Date | Stars |
|---|---|
| 2026-07-24 | 2410 |
| 2026-07-25 | 2410 |
| 2026-07-28 | 2410 |
| 2026-07-30 | 2410 |
| 2026-07-31 | 2410 |
| 2026-08-06 | 2410 |
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<div align="center"> <img src="assets/mPLUG_new1.png" width="80%"> </div> <div align="center"> <h2>The Powerful Multi-modal LLM Family for OCR-free Document Understanding<h2> <strong>Alibaba Group</strong> </div> <p align="center"> <a href="https://trendshift.io/repositories/9061" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9061" alt="DocOwl | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p> ## 📢 News * 🔥🔥🔥 [2025.5.16] Our paper [DocOwl2](https://arxiv.org/abs/2409.03420) is accepted by ACL 2025. * 🔥🔥🔥 [2024.12.24] We have released the training code of DocOwl2 by [**ms-swift**](https://github.com/modelscope/ms-swift)! Now you can finetune a stronger model with your own data based on DocOwl2! * 🔥🔥🔥 [2024.9.28] We have released the training data, inference code and evaluation code of [DocOwl2](./DocOwl2/) on both **HuggingFace** 🤗 and **ModelScope** <img src="./assets/modelscope.png" width='20'>. * 🔥🔥🔥 [2024.9.20] Our paper [DocOwl 1.5](http://arxiv.org/abs/2403.12895) and [TinyChart](https://arxiv.org/abs/2404.16635) is accepted by EMNLP 2024. * 🔥🔥 [2024.9.06] We release the arxiv paper of [mPLUG-DocOwl 2](https://arxiv.org/abs/2409.03420), a SOTA 8B Multimodal LLM on OCR-free Multipage Document Understanding, each document image is encoded with just 324 tokens! * [2024.7.16] Our paper [PaperOwl](https://arxiv.org/abs/2311.18248) is accepted by ACM MM 2024. * [2024.5.08] We have released the training code of [DocOwl1.5](./DocOwl1.5/) supported by DeepSpeed. You can now finetune a stronger model based on DocOwl1.5! * [2024.4.26] We release the arxiv paper of [TinyChart](https://arxiv.org/abs/2404.16635), a SOTA 3B Multimodal LLM for Chart Understanding with Program-of-Throught ability (ChartQA: 83.6 > Gemin-Ultra 80.8 > GPT4V 78.5). The demo of TinyChart is available on [HuggingFace](https://huggingface.co/spaces/mPLUG/TinyChart-3B) 🤗. Both codes, models and data are released in [TinyChart](./TinyChart/). * [2024.4.3] We build demos of DocOwl1.5 on both [ModelScope](https://modelscope.cn/studios/iic/mPLUG-DocOwl/) <img src="./assets/modelscope.png" width='20'> and [HuggingFace](https://huggingface.co/spaces/mPLUG/DocOwl) 🤗, supported by the DocOwl1.5-Omni. The source codes of launching a local demo are also released in [DocOwl1.5](./DocOwl1.5/). * [2024.3.28] We release the training data (DocStruct4M, DocDownstream-1.0, DocReason25K), codes and models (DocOwl1.5-stage1, DocOwl1.5, DocOwl1.5-Chat, DocOwl1.5-Omni) of [mPLUG-DocOwl 1.5](./DocOwl1.5/) on both **HuggingFace** 🤗 and **ModelScope** <img src="./assets/modelscope.png" width='20'>. * [2024.3.20] We release the arxiv paper of [mPLUG-DocOwl 1.5](http://arxiv.org/abs/2403.12895), a SOTA 8B Multimodal LLM on OCR-free Document Understanding (DocVQA 82.2, InfoVQA 50.7, ChartQA 70.2, TextVQA 68.6). * [2024.01.13] Our Scientific Diagram Analysis dataset [M-Paper](https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/PaperOwl) has been available on both **HuggingFace** 🤗 and **ModelScope** <img src="./assets/modelscope.png" width='20'>, containing 447k high-resolution diagram images and corresponding paragraph analysis. * [2023.10.13] Training data, models of [mPLUG-DocOwl](./DocOwl/)/[UReader](./UReader/) has been open-sourced. * [2023.10.10] Our paper [UReader](https://arxiv.org/abs/2310.05126) is accepted by EMNLP 2023. <!-- * 🔥 [10.10] The source code and instruction data will be released in [UReader](https://github.com/LukeForeverYoung/UReader). --> * [2023.07.10] The demo of mPLUG-DocOwl on [ModelScope](https://modelscope.cn/studios/damo/mPLUG-DocOwl/summary) is avaliable. * [2023.07.07] We release the technical report and evaluation set of mPLUG-DocOwl. ## 🤖 Models - [**mPLUG-DocOwl2**](./DocOwl2/) (Arxiv 2024) - mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding - [**mPLUG-DocOwl1.5**](./DocOwl1.5/) (EMNLP 2024) - mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free
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叶加博 · Tongyi, Alibaba Group · China
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xuhaiyang-mPLUG · Alibaba DAMO Academy · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:92a36ca72cb397c7, topic:multimodal, topic:document-understanding, desc:multimodal