Instructions to use IVGSZ/Flash-VStream-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IVGSZ/Flash-VStream-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IVGSZ/Flash-VStream-7b")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IVGSZ/Flash-VStream-7b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use IVGSZ/Flash-VStream-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IVGSZ/Flash-VStream-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IVGSZ/Flash-VStream-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IVGSZ/Flash-VStream-7b
- SGLang
How to use IVGSZ/Flash-VStream-7b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "IVGSZ/Flash-VStream-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IVGSZ/Flash-VStream-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "IVGSZ/Flash-VStream-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IVGSZ/Flash-VStream-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IVGSZ/Flash-VStream-7b with Docker Model Runner:
docker model run hf.co/IVGSZ/Flash-VStream-7b
| 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). | |