---
license: apache-2.0
pipeline_tag: image-text-to-text
---

**<center><span style="font-size:2em;">TinyLLaVA-Video</span></center>**

[![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2501.15513)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/ZhangXJ199/TinyLLaVA-Video)


Here, we introduce TinyLLaVA-Video-Phi2-16-512. For LLM and vision tower, we choose [Phi-2](https://huggingface.co/microsoft/phi-2) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The model samples 16 frames from each video and represents the video sequence using 512 tokens. 

### Result
|                VT (HF Path)                | #Frame/Query |   Video-MME   | MVBench | LongVideoBench |    MLVU    | 
| :----------------------------------------: | ------------ | ------------- | ------- | -------------- | ---------- | 
| [Zhang199/TinyLLaVA-Video-Qwen2.5-3B-16-512](https://huggingface.co/Zhang199/TinyLLaVA-Video-Qwen2.5-3B-16-512)    |    16/512    |    44.7   |   42.5  |      37.6      |  48.1  | 
| [Zhang199/TinyLLaVA-Video-Phi2-16-512](https://huggingface.co/Zhang199/TinyLLaVA-Video-Phi2-16-512)    |    16/512    |    42.7   |   42.0  |      42.2      |  46.5  |