---
license: mit
---
![header](./assets/header.png) 

<p align="center">
   📃 <a href="" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 📃 <a href="https://github.com/FreedomIntelligence/LongLLaVA" target="_blank">LongLLaVA</a> 
</p>

![efficiency](./assets/singleGPU.png) 

## 🌈 Update

* **[2024.09.05]** LongLLaVA repo is published！🎉 The Code will

## Architecture

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

  ![Architecture Image](./assets/arch.png)

</details>


## Results

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

  - Main Results
      ![Main Results](./assets/result1.png) 
  - Diagnostic Results
      ![Diagnostic Results](./assets/diaresult.png)
  - Video-NIAH
      ![Video-NIAH](./assets/NIAH.png)

</details>



## Results reproduction

### Data DownLoad and Construction

<details>
  <summary>Dataset Taxonomy</summary>

  ![Dataset](./assets/dataset.png) 

</details>

<details>
  <summary>Dataset DownLoading and Construction</summary>

   > Coming Soon~

</details>



### Evaluation

> Model checkpoint is Coming Soon~



## 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}, 
}
```