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---
license: apache-2.0
tags:
- vision-language model
- llama
- video understanding
pipeline_tag: video-text-to-text
library_name: transformers
---
# Flash-VStream Model Card
This repository contains the Flash-VStream model presented in the paper [Flash-VStream: Efficient Real-Time Understanding for Long Video Streams](https://huggingface.co/papers/2506.23825).
<a href='https://zhang9302002.github.io/vstream-iccv-page/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a href='https://huggingface.co/papers/2506.23825'><img src='https://img.shields.io/badge/Paper-HuggingFace-red'></a>
<a href='https://github.com/IVGSZ/Flash-VStream'><img src='https://img.shields.io/badge/Code-GitHub-blue.svg?logo=github'></a>
## Model details
We proposed Flash-VStream, a video-language model that simulates the memory mechanism of human. Our model is able to process extremely long video streams in real-time and respond to user queries simultaneously.
## Training data
This model is trained based on image data from LLaVA-1.5 dataset, and video data from WebVid and ActivityNet datasets following LLaMA-VID, including
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data.
- 232K video-caption pairs sampled from the WebVid 2.5M dataset.
- 98K videos from ActivityNet with QA pairs from Video-ChatGPT.
## Sample Usage
You can load and use Flash-VStream with the `transformers` library.
```python
import torch
from transformers import AutoModel, AutoTokenizer
# The model can be loaded using multiple GPUs or offloaded to CPU if needed.
# This example assumes GPU is available.
model_path = 'IVGSZ/Flash-VStream-7b' # Replace with the actual model ID if different
model = AutoModel.from_pretrained(
model_path,
torch_dtype=torch.bfloat16, # Use bfloat16 for efficient memory usage
low_cpu_mem_usage=True,
trust_remote_code=True
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
# For detailed instructions on image/video preprocessing and chat interactions,
# please refer to the official GitHub repository:
# https://github.com/IVGSZ/Flash-VStream
```
## License
This project is licensed under the [Apache-2.0 License](https://github.com/IVGSZ/Flash-VStream/blob/main/LICENSE). |