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StreamingChat 8B

This dataset card aims to provide a comprehensive overview of the StreamingChat model. For details, see our Project, Paper, Dataset and GitHub repository.

TipoModeloModelo baseOpenGVLab/InternVideo2_5_Chat_8BVersiónRevision 57035afBcalidad

Fuente del modelo

Tipo
Modelo
Tamaño del archivo
1,28–4,60 GB
Modelo base
OpenGVLab/InternVideo2_5_Chat_8B
Versión
vRevision 57035af
Actualizado
30 oct 2026
Descargas
31
Fuente
Hugging Face

Descripción de la fuente

Model Card for StreamingChat

This dataset card aims to provide a comprehensive overview of the StreamingChat model. For details, see our Project, Paper, Dataset and GitHub repository.

Dataset Description

StreamingChat is a streaming video understanding model built upon InternVideo2.5. It utilizes Streaming video dialogue data, including temporal dialogue paths from the SVBench training set. The model is fine-tuned using a static resolution strategy, enabling it to process several minutes of video at a rate of 1 FPS. Images are interleaved with language tokens, with each image comprising 16 tokens. This model aims to catalyze progress in streaming video understanding.

Uses

Download the StreamingChat model from Hugging Face:

git clone https://huggingface.co/yzy666/StreamingChat_8B

Install Python dependencies:

conda create -n StreamingChat -y python=3.9.21
conda activate StreamingChat
conda install -y -c pytorch pytorch=2.5.1 torchvision=0.10.1
pip install transformers=4.37.2 opencv-python=4.11.0.84 imageio=2.37.0 decord=0.6.0
pip install flash-attn --no-build-isolation

Run the inference script directly:

python demo.py

Citation

If you find our data useful, please consider citing our work!

@article{yang2025svbench,
  title={SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding},
  author={Yang, Zhenyu and Hu, Yuhang and Du, Zemin and Xue, Dizhan and Qian, Shengsheng and Wu, Jiahong and Yang, Fan and Dong, Weiming and Xu, Changsheng},
  journal={arXiv preprint arXiv:2502.10810},
  year={2025}
}

Fuentes

1 fuente
Hugging FacePrincipalVersión 57035af69ae1c518d48c0987e77847cebcfa019e
huggingface.co/yzy666/StreamingChat_8B/tree/57035af69ae1c518d48c0987e77847cebcfa019e

Verificado 30 oct

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Model · 4 weight files

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Artefactos del modelo

4 artefactos

model-00001-of-00004.safetensors

safetensors · 4,60 GB · SHA-256 30439a24b50a…3254 · Hugging Face

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model-00002-of-00004.safetensors

safetensors · 4,58 GB · SHA-256 57751e12015d…ed4a · Hugging Face

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model-00003-of-00004.safetensors

safetensors · 4,58 GB · SHA-256 b18f13dc1312…fbb1 · Hugging Face

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model-00004-of-00004.safetensors

safetensors · 1,28 GB · SHA-256 0965077070b3…f3d4 · Hugging Face

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Extractos de fuentes

3 extractos

--- license: apache-2.0 datasets: - yzy666/SVBench language: - en metrics: - code_eval base_model: - OpenGVLab/InternVideo2_5_Chat_8B pipeline_tag: visual-question-answering --- # Model Card for StreamingChat <!-- Provide a quick summary of what the model is/does. --> This dataset card aims to provide a comprehensive overview of the StreamingChat model. For details, see our [Project](https://yzy-bupt.github.io/SVBench/), [Paper](https://arxiv.org/abs/2502.10810), [Dataset](https://huggingface.co/datasets/yzy666/SVBench) and [GitHub repository](https://github.com/yzy-bupt/SVBench). ## **Dataset Description** **StreamingChat** is a streaming video understanding model built upon [InternVideo2.5](https://huggingface.co/OpenGVLab/InternVideo2_5_Chat_8B). It utilizes Streaming video dialogue data, including temporal dialogue paths from the [SVBench](https://huggingface.co/datasets/yzy666/SVBench) training set. The model is fine-tuned using a static resolution strategy, enabling it to process several minutes of video at a rate of 1 FPS. Images are interleaved with language tokens, with each image comprising 16 tokens. This model aims to catalyze progress in streaming video understanding. ## **Uses** Download the StreamingChat model from Hugging Face: ```bash git clone https://huggingface.co/yzy666/StreamingChat_8B ``` Install Python dependencies: ```bash conda create -n StreamingChat -y python=3.9.21 conda activate StreamingChat conda install -y -c pytorch pytorch=2.5.1 torchvision=0.10.1 pip install transformers=4.37.2 opencv-python=4.11.0.84 imageio=2.37.0 decord=0.6.0 pip install flash-attn --no-build-isolation ``` Run the inference script directly: ```bash python demo.py ``` ## **Citation** If you find our data useful, please consider citing our work! ``` @article{yang2025svbench, title={SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding}, author={Yang, Zhenyu and Hu, Yuhang and Du, Zemin and Xue, Dizhan and Qian, Shengsheng and Wu, Jiahong and Yang, Fan and Dong, Weiming and Xu, Changsheng}, journal={arXiv preprint arXiv:2502.10810}, year={2025} } ```

Source context: 31 downloads · 1 likes · Pipeline visual-question-answering · Repo yzy666/StreamingChat_8B

Source context: 25 downloads · 1 likes · Pipeline visual-question-answering · Repo yzy666/StreamingChat_8B