Improve model card: Add pipeline tag, library, update links and license

README.md

@@ -1,27 +1,59 @@
----
-license: llama2
-tags:
-- vision-language model
-- llama
-- video understanding
----
-
-# Flash-VStream Model Card
-<a href='https://invinciblewyq.github.io/vstream-page/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> 
-<a href='https://arxiv.org/abs/2406.08085v1'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></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.
-
-## License
-
-This project is licensed under the [LLAMA 2 License](LICENSE).
+---
+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).