📃 Paper • 🌐 Demo • 🤗 LongLLaVA
- [2024.09.05] LongLLaVA repo is published!🎉 The Code will
pip install -r requirements.txt
- Dataset DownLoading and Construction
Coming Soon.
- Stage I: Single-image Alignment.
bash Align.sh
- Stage II: Single-image Instruction-tuning.
bash SingleImageSFT.sh
- Stage III: Multi-image Instruction-tuning.
bash MultiImageSFT.sh
- Command Line Interface
python cli.py --model_dir path-to-longllava
- Model Inference
query = 'What does the picture show?'
image_paths = ['image_path1'] # image or video path
from cli import Chatbot
bot = Chatbot(path-to-longllava)
output = bot.chat(query, image_paths)
print(output) # Prints the output of the model
- Benchmarks
python Eval.sh
- FLOPs
python /utils/cal_flops.py
- Prefill Time & Throughput & GPU Memory Usage
python ./benchmarks/Efficiency/evaluate.py
python ./benchmarks/Efficiency/evaluatevllm.py
- Release Data Construction Code
- LLaVA: Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
@misc{wang2024longllavascalingmultimodalllms,
title={LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via Hybrid Architecture},
author={Xidong Wang and Dingjie Song and Shunian Chen and Chen Zhang and Benyou Wang},
year={2024},
eprint={2409.02889},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.02889},
}