Any-to-Any
Transformers
Safetensors
qwen3_5
image-text-to-text
video-editing
agent
tool-use
long-horizon
reinforcement-learning
qwen3.5
Instructions to use tencent/WeVisCut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/WeVisCut with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tencent/WeVisCut") model = AutoModelForMultimodalLM.from_pretrained("tencent/WeVisCut", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/overview.png from tencent/WeVisCut: direct link, hf CLI and curl.
- Browser
- Download file 849 kB
-
https://huggingface.co/tencent/WeVisCut/resolve/main/assets/overview.png
- Command line
-
hf download hf://tencent/WeVisCut/assets/overview.png
-
curl -L -o overview.png https://huggingface.co/tencent/WeVisCut/resolve/main/assets/overview.png
849 kB

- Xet hash:
- a0b8412f64d6903be377dae92020b165a7cfc189b2a264fa80c5ef86638068dd
- Size of remote file:
- 849 kB
- SHA256:
- c03e3e09042d0495dddd817ca2e5b9d716576177aa649d89fe1aa4c094056290
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.