LongLLaVA-53B-A13B

<p align="center"> 📃 <a href="https://arxiv.org/abs/2409.02889" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 📃 <a href="https://github.com/FreedomIntelligence/LongLLaVA" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/LongLLaVA-9B" target="_blank">LongLLaVA-9B</a> </p>

🌈 Update

• [2024.09.05] LongLLaVA repo is published！🎉
• [2024.10.12] LongLLaVA-53B-A13B, LongLLaVA-9b and Jamba-9B-Instruct are repleased！🎉

Architecture

<details> <summary>Click to view the architecture image</summary>

</details>

Results

<details> <summary>Click to view the Results</summary>

• Main Results
• Diagnostic Results
• Video-NIAH

</details>

Results reproduction

Evaluation

• Preparation

Get the model inference code from Github.

git clone https://github.com/FreedomIntelligence/LongLLaVA.git

• Environment Setup

pip install -r requirements.txt

• 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

Acknowledgement

• LLaVA: Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

Citation

@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}, 
}