base on Official code implementation of Vary-toy (Small Language Model Meets with Reinforced Vision Vocabulary) <h3><a href="">Small Language Model Meets with Reinforced Vision Vocabulary</a></h3> <a href="https://varytoy.github.io/"><img src="https://img.shields.io/badge/Project-Page-Green"></a> <a href="https://arxiv.org/abs/2401.12503"><img src="https://img.shields.io/badge/Paper-PDF-orange"></a> <a href="https://vary.xiaomy.net/"><img src="https://img.shields.io/badge/demo-blue"></a> <a href="https://zhuanlan.zhihu.com/p/679447793"><img src="https://img.shields.io/badge/zhihu-yellow"></a> <a href="https://trendshift.io/repositories/7311" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7311" alt="Ucas-HaoranWei%2FVary-toy | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> [Haoran Wei*](https://scholar.google.com/citations?user=J4naK0MAAAAJ&hl=en), Lingyu Kong*, Jinyue Chen, Liang Zhao, [Zheng Ge](https://joker316701882.github.io/), [En Yu](https://scholar.google.com.hk/citations?user=rWCQMNgAAAAJ&hl=zh-CN&oi=sra), [Jianjian Sun](https://scholar.google.com/citations?user=MVZrGkYAAAAJ&hl=en), Chunrui Han, [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en) <p align="center"> <img src="assets/vary-toy-logo.jpg" style="width: 200px" align=center> </p> <p align="center"> <a href="">The Young's First ``Large'' Vision Language Model</a> </p> ## Release - [2024/9/03] 🔥🔥🔥 We release a very strong and comprehensive OCR model [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0). - [2024/7/21] 🎉🎉🎉 OneChart is accepted by ACM'MM 2024 **Oral**! (3.97%) - [2024/7/2] 🔥🔥🔥 Vary is accepted by ECCV2024. To thank everyone for their attention, I will release a model that performs on par with the Vary-document soon. - [2024/5/27] 🔥🔥🔥 We present a document understanding benchmark in [Fox](https://github.com/ucaslcl/Fox) . - [2024/5/24] 🔥🔥🔥 We propose a multi-page document understanding work -- [Fox](https://arxiv.org/abs/2405.14295), which supports 8-page pdf-image input !!! - [2024/4/21] 🔥🔥🔥 For OneChart, we have released the web demo in [Project Page](https://onechartt.github.io/). Have fun!! - [2024/4/21] 🔥🔥🔥 We present a Vary-tiny LAVIS codebase (for training from scratch) and the Vary-600k dataset (300K English and 300K Chinese pages) [here](https://github.com/Ucas-HaoranWei/Vary-tiny-600k) !!! - [2024/4/15]🔥🔥🔥We release a chart parsing model OneChart [here](https://github.com/LingyvKong/OneChart). - [2024/4/12]🔥🔥🔥We will release a chart parsing model based on Vary-tiny next week. The model supports both English and Chinese charts. - [2024/3/16]🔥🔥🔥I found many friends very interested in Vary-tiny(OPT-125M), so I opened source it [here](https://huggingface.co/HaoranWei/Vary-tiny-opt125M/tree/main), a PDF-dense OCR and object detection version. - [2024/1/23] 🔥Eval codes will be available soon. - [2024/1/23] 🔥🔥🔥You only need a single 1080Ti to experience all features of current LVLMs. [](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE) [](https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE) **Usage and License Notices**: The data, code, and checkpoint are intended and licensed for research use only. They are also restricted to use that follow the license agreement of LLaMA, Vicuna, GPT-4, Qwen, and LLaVA. ## Contents - [Install](#install) - [Vary-toy Weights](#vary-weights) - [Demo](#Demo) - [Train](#train) ## Note If you have built the original [Vary](https://github.com/Ucas-HaoranWei/Vary), please rebuild this repo !!! ## Install 1. Clone this repository and navigate to the Vary folder ```bash git clone https://github.com/Ucas-HaoranWei/Vary-toy.git cd /path/to/vary-toy ``` 2. Install Package ```Shell conda create -n vary python=3.10 -y conda activate vary pip install e . ``` 3. Install Flash-Attention ``` pip install ninja pip install flash-attn --no-build-isolation ``` ## Vary Weights - Download the Vary-toy weights [here](https://huggingface.co/Haoran-megvii/Vary-toy). - Download the CLIP-VIT-L [here](https://huggingface.co/openai/clip-vit-large-patch14/). ## Demo 1. Update the CLIP-VIT path in the codes (/cache/vit-large-patch14/) to your path. 2. ```Shell python vary/demo/run_qwen_vary.py --model-name /vary/model/path/ --image-file /an/image/file.png ``` ## Train ```Shell deepspeed Vary/train/train_qwen_vary.py --deepspeed /Vary/zero_config/zero2.json --model_name_or_path /Vary-toy/path/ --vision_tower /vit-large-patch14/path/ --freeze_vision_tower True --freeze_lm_model False --vision_select_layer -2 --use_im_start_end True --bf16 True --per_device_eval_batch_size 4 --gradient_accumulation_steps 1 --evaluation_strategy "no" --save_strategy "steps" --save_steps 5000 --save_total_limit 1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type "cosine" --logging_steps 1 --tf32 True --model_max_length 4096 --gradient_checkpointing True --dataloader_num_workers 4 --report_to none --per_device_train_batch_size 4 --num_train_epochs 1 --learning_rate 5e-5 --datasets data_name1+data_name2+data_name3 --output_dir /path/to/output/ ``` We encourage you to extract the new vision vocabulary weights for your new base language model !!! ## Contact If you have any questions about the code or the paper, please email (`[email protected]`). ## Discussion Vary-toy is not a toy, and we have designed two excellent models based on it, one is Vary-document (specifically for document/pdf processing), and the other is Vary-plot for chart analysis. You can see their amazing performance here [Vary-family](https://github.com/Ucas-HaoranWei/Vary-family). ## Citation If you find our work useful in your research, please consider citing Vary: ```bibtex @article{wei2023vary, title={Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models}, author={Wei, Haoran and Kong, Lingyu and Chen, Jinyue and Zhao, Liang and Ge, Zheng and Yang, Jinrong and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu}, journal={arXiv preprint arXiv:2312.06109}, year={2023} } @article{wei2024small, title={Small Language Model Meets with Reinforced Vision Vocabulary}, author={Wei, Haoran and Kong, Lingyu and Chen, Jinyue and Zhao, Liang and Ge, Zheng and Yu, En and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu}, journal={arXiv preprint arXiv:2401.12503}, year={2024} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"7311","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"