Instructions to use WRHC/EfficientVideoAgent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WRHC/EfficientVideoAgent with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("WRHC/EfficientVideoAgent") model = AutoModelForMultimodalLM.from_pretrained("WRHC/EfficientVideoAgent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download fig1.png from WRHC/EfficientVideoAgent: direct link, hf CLI and curl.
- Browser
- Download file 1.17 MB
-
https://huggingface.co/WRHC/EfficientVideoAgent/resolve/main/fig1.png
- Command line
-
hf download hf://WRHC/EfficientVideoAgent/fig1.png
-
curl -L -o fig1.png https://huggingface.co/WRHC/EfficientVideoAgent/resolve/main/fig1.png
1.17 MB

- Xet hash:
- 0c65b33d85db8eb4d3362b01957c4d797f666c9daf256dd996bed335c266303b
- Size of remote file:
- 1.17 MB
- SHA256:
- bb6a723e8d9863a293fd7d2bdd8ea8e994ba146598f947e6fb9c95ed2b44d5e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.