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JosephTong/
llava-v1.5-7b-flowcut128

Name: llava-v1.5-7b-flowcut128
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FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models

Jintao Tong¹, Wenwei Jin², Pengda Qin², Anqi Li³, Yixiong Zou^1✉ Yuhong Li^2✉, Yuhua Li¹, Ruixuan Li¹

¹School of Computer Science and Technology, Huazhong University of Science and Technology
²Xiaohongshu Inc., ³Institute of Information Science, Beijing Jiaotong University

GitHub

arXiv

License

💡 Highlights

TLDR: To address inefficiency from excessive visual tokens in LVLMs, we propose a unified, bottom-up perspective based on information-flow, revealing dynamic redundancy emergence and introduce FlowCut, making pruning decision aligned with the model's inherent behavior, outperforming all existing approaches.

🛠 Preparation

Our code is easy to use.

Clone the LLaVA's repository.

git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA

Install the LLaVA's environment.

conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip  
pip install -e .
pip install flash-attn --no-build-isolation

For formal usage, you can install the package from PyPI by running the following command:

pip install flowcut

For development, you can install the package by cloning the repository and running the following command:

git clone https://github.com/TungChintao/FlowCut
cd flowcut
pip install -e .

File organization as follow:

├── LLaVA-main
    ├── flowcut
    ├── llava
    ├── playground
    ├── script

🚀 Quick Start

Python

from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava import eval_model
from flowcut import flowcut
model_path = "liuhaotian/llava-v1.5-7b"

tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path=model_path,
    model_base=None,
    model_name=get_model_name_from_path(model_path)
)
## FlowCut retains 64 visual tokens
model = flowcut(model, target_num=64)

📖 Evaluation

The evaluation code follows the structure of LLaVA or Lmms-Eval. After loading the model, simply add two lines as shown below:

python

## Load LLaVA Model (code from llava.eval.model_vqa_loader)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
## add FlowCut
from flowcut import flowcut
model = flowcut(model, target_num=64)

Script templetes (please follow the detailed instruction in LLaVA-Evaluation).

bash scripts/v1_5/eval/[Benchmark].sh

Examples:

CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mme.sh

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/vqav2.sh

🎯 Training

The training code follows the structure of LLaVA. After loading the model, simply add two lines as shown below: