---
license: gpl-3.0
---
# LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
[](https://arxiv.org/abs/2501.03895)
[](https://huggingface.co./ICTNLP/llava-mini-llama-3.1-8b)
> **[Shaolei Zhang](https://zhangshaolei1998.github.io/), [Qingkai Fang](https://fangqingkai.github.io/), [Zhe Yang](https://nlp.ict.ac.cn/yjdw/xs/ssyjs/202210/t20221020_52708.html), [Yang Feng*](https://people.ucas.edu.cn/~yangfeng?language=en)**
LLaVA-Mini is a unified large multimodal model that can support the understanding of images, high-resolution images, and videos in an efficient manner. Guided by the interpretability within LMM, LLaVA-Mini significantly improves efficiency while ensuring vision capabilities. [Code](https://github.com/ictnlp/LLaVA-Mini), [model](https://huggingface.co./ICTNLP/llava-mini-llama-3.1-8b) and [demo](https://github.com/ictnlp/LLaVA-Mini#-demo) of LLaVA-Mini are available now!
Refer to our [GitHub repo]((https://github.com/ictnlp/LLaVA-Mini)) for details of LLaVA-Mini!
> [!Note]
> LLaVA-Mini only requires **1 token** to represent each image, which improves the efficiency of image and video understanding, including:
> - **Computational effort**: 77% FLOPs reduction
> - **Response latency**: reduce from 100 milliseconds to 40 milliseconds
> - **VRAM memory usage**: reduce from 360 MB/image to 0.6 MB/image, support 3-hour video processing
💡**Highlight**:
1. **Good Performance**: LLaVA-Mini achieves performance comparable to LLaVA-v1.5 while using only 1 vision token instead of 576 (compression rate of 0.17%).
2. **High Efficiency**: LLaVA-Mini can reduce FLOPs by 77%, deliver low-latency responses within 40 milliseconds, and process over 10,000 frames of video on the GPU hardware with 24GB of memory.
3. **Insights**: To develop LLaVA-Mini, which reduces vision tokens while maintaining visual understanding, we conduct a preliminary analysis to explore how large multimodal models (LMMs) process visual tokens. Please refer to our [paper](https://arxiv.org/pdf/2501.03895) for a detailed analysis and our conclusions.
## 🖥 Demo
- Download LLaVA-Mini model from [here](https://huggingface.co./ICTNLP/llava-mini-llama-3.1-8b).
- Run these scripts and Interact with LLaVA-Mini in your browser:
```bash
# Launch a controller
python -m llavamini.serve.controller --host 0.0.0.0 --port 10000 &
# Build the API of LLaVA-Mini
CUDA_VISIBLE_DEVICES=0 python -m llavamini.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path ICTNLP/llava-mini-llama-3.1-8b --model-name llava-mini &
# Start the interactive interface
python -m llavamini.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --port 7860
```
## 🔥 Quick Start
### Requirements
- Install packages:
```bash
conda create -n llavamini python=3.10 -y
conda activate llavamini
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
```
### Command Interaction
- Image understanding, using `--image-file `:
```bash
# Image Understanding
CUDA_VISIBLE_DEVICES=0 python llavamini/eval/run_llava_mini.py \
--model-path ICTNLP/llava-mini-llama-3.1-8b \
--image-file llavamini/serve/examples/baby_cake.png \
--conv-mode llava_llama_3_1 --model-name "llava-mini" \
--query "What's the text on the cake?"
```
- Video understanding, using `--video-file `:
```bash
# Video Understanding
CUDA_VISIBLE_DEVICES=0 python llavamini/eval/run_llava_mini.py \
--model-path ICTNLP/llava-mini-llama-3.1-8b \
--video-file llavamini/serve/examples/fifa.mp4 \
--conv-mode llava_llama_3_1 --model-name "llava-mini" \
--query "What happened in this video?"
```
### Reproduction and Evaluation
- Refer to [Evaluation.md](docs/Evaluation.md) for the evaluation of LLaVA-Mini on image/video benchmarks.
### Cases
- LLaVA-Mini achieves high-quality image understanding and video understanding.
More cases
- LLaVA-Mini dynamically compresses image to capture important visual information (brighter areas are more heavily weighted during compression).
## 🖋Citation
If this repository is useful for you, please cite as:
```
@misc{llavamini,
title={LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token},
author={Shaolei Zhang and Qingkai Fang and Zhe Yang and Yang Feng},
year={2025},
eprint={2501.03895},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.03895},
}
```
If you have any questions, please feel free to submit an issue or contact `zhangshaolei20z@ict.ac.cn`.