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/model_architecture.png from tencent/WeVisCut: direct link, hf CLI and curl.
- Browser
- Download file 1.78 MB
-
https://huggingface.co/tencent/WeVisCut/resolve/main/assets/model_architecture.png
- Command line
-
hf download hf://tencent/WeVisCut/assets/model_architecture.png
-
curl -L -o model_architecture.png https://huggingface.co/tencent/WeVisCut/resolve/main/assets/model_architecture.png
1.78 MB

- Xet hash:
- 32fe01d50c7516f58eb6a45ec13a7d37abd80dd22735a0c1494e1f82deeb4aa3
- Size of remote file:
- 1.78 MB
- SHA256:
- 69d146e0192f1f12e8f809bb3c7d40a614db178675f8af104dc582d34cf04673
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