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Small Language Model Meets with Reinforced Vision Vocabulary

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

The Young's First ``Large'' Vision Language Model

Release

  • [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.

Code License 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

Note

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

Install

  1. Clone this repository and navigate to the Vary folder
git clone https://github.com/Ucas-HaoranWei/Vary-toy.git
cd /path/to/vary-toy
  1. Install Package
conda create -n vary python=3.10 -y
conda activate vary
pip install e .
  1. 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

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

cd Vary-master/
python vary/demo/run_qwen_vary.py  --model-name  /home/lingyuzeng/workdir/project/Vary-toy/Varyweight --image-file /home/lingyuzeng/workdir/project/Vary-toy/fork/Vary-toy/1706251406013.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 ([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.

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}
}

device requirement

support GPU bfloat16 training and inference.

does not support GPU V100, T4.

The NVIDIA T4 GPU does not support bfloat16 natively, as indicated in a comparison table that mentions Nvidia Volta (V100) and Turing (T4) do not support bfloat16, while Nvidia Ampere (A100) does​​. Therefore, if your application or model requires bfloat16 precision, it would be advisable to use a GPU from the Ampere series, such as the A100, which provides native support for bfloat16.

RUN api restful server

cd Vary-master/
pip install e .
# update the CLIP_MODEL_PATH and MODEL_NAME
export MODEL_NAME=/path/to/Varyweight
export CLIP_MODEL_PATH=/path/to/Vary-toy/clip-vit-large-patch14/
micromamba run -n varytoy python -m vary.api --host 0.0.0.0 --port 58616

test api:

import requests
url = "http://127.0.0.1:58616/eval-image/"
file_path = "Vary-master/vary/demo/1706251406013.png"
files = {"file": open(file_path, "rb")}
data = {"token": "secret-token"}
response = requests.post(url, files=files, data=data)
print(response.json())
print(response.status_code)
# or 
curl -X POST -F "file=@Vary-master/vary/demo/1706251406013.png" -F "token=secret-token" http://127.0.0.1:58616/eval-image/

use curl:

curl -X POST -F "token=secret-token" -F "file=@Vary-master/vary/demo/1706251406013.png" http://127.0.0.1:58616/eval-image/

or run with docker:

first to install Nvidia GPU:

sudo curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
sudo curl -s -L https://nvidia.github.io/nvidia-container-runtime/$distribution/nvidia-container-runtime.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list
sudo apt-get update

sudo apt-get install nvidia-container-runtime
sudo nvidia-ctk runtime configure --runtime=docker
which nvidia-container-runtime

then run docker-compose:

git repo:

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

mv Vary-toy/ Varyweight

change docker-compose.yml volume path:

    volumes:
      - ./clip-vit-large-patch14:/app/Vary-master/clip-vit-large-patch14
      - ./Varyweight:/app/Vary-master/Varyweight

then run:

docker-compose up -d