Awesome

<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>

Haoran Wei*, Lingyu Kong*, Jinyue Chen, Liang Zhao, Zheng Ge, En Yu, Jianjian Sun, Chunrui Han, Xiangyu Zhang

<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.
[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 .
[2024/5/24] 🔥🔥🔥 We propose a multi-page document understanding work -- Fox, which supports 8-page pdf-image input !!!
[2024/4/21] 🔥🔥🔥 For OneChart, we have released the web demo in Project Page. 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 !!!
[2024/4/15]🔥🔥🔥We release a chart parsing model OneChart here.
[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, 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.

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.

Install
Vary-toy Weights
Demo
Train

Note

If you have built the original Vary, please rebuild this repo !!!

Install

Clone this repository and navigate to the Vary folder

git clone https://github.com/Ucas-HaoranWei/Vary-toy.git
cd /path/to/vary-toy

Install Package

conda create -n vary python=3.10 -y
conda activate vary
pip install e .

Install Flash-Attention

pip install ninja
pip install flash-attn --no-build-isolation

Vary Weights

Download the Vary-toy weights here.
Download the CLIP-VIT-L here.

Demo

Update the CLIP-VIT path in the codes (/cache/vit-large-patch14/) to your path.

python vary/demo/run_qwen_vary.py  --model-name  /vary/model/path/ --image-file /an/image/file.png

Train

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 (weihaoran18@mails.ucas.ac.cn).

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.

Citation

If you find our work useful in your research, please consider citing Vary:

@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}
}