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/introduction.png from tencent/WeVisCut: direct link, hf CLI and curl.
- Browser
- Download file 1.8 MB
-
https://huggingface.co/tencent/WeVisCut/resolve/main/assets/introduction.png
- Command line
-
hf download hf://tencent/WeVisCut/assets/introduction.png
-
curl -L -o introduction.png https://huggingface.co/tencent/WeVisCut/resolve/main/assets/introduction.png
1.8 MB

- Xet hash:
- 58de89c4fc6e2d2bcd5b33753ab36f65c357380d3c42210d9922afdfe6e560de
- Size of remote file:
- 1.8 MB
- SHA256:
- e41693d5841f0b539eadd722ecb42ca583fdbb39b331597b5ed6b41694b51d27
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