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
metadata
license: llama2
tags:
- vision-language model
- llama
- video understanding
Flash-VStream Model Card
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.