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/training_results.png from tencent/WeVisCut: direct link, hf CLI and curl.
- Browser
- Download file 319 kB
-
https://huggingface.co/tencent/WeVisCut/resolve/main/assets/training_results.png
- Command line
-
hf download hf://tencent/WeVisCut/assets/training_results.png
-
curl -L -o training_results.png https://huggingface.co/tencent/WeVisCut/resolve/main/assets/training_results.png
319 kB

- Xet hash:
- b9fd610b5f8bfcbb42b7a1455b1477b92d4fa98171881623a6db7e8a1f601d80
- Size of remote file:
- 319 kB
- SHA256:
- 5b6e341fa8b59a82f84e4c6866f34d20a4b2e1efee52f6137cee5f8a95da2f8c
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