Transformers
PyTorch
multilingual
seamless_crossattention
audio
text
multimodal
seamless
subtitle-editing-time-prediction
cross-attention
attention-mechanism
Instructions to use videoloc/seamless-crossattention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use videoloc/seamless-crossattention with Transformers:
# Load model directly from transformers import HFSeamlessCrossAttention model = HFSeamlessCrossAttention.from_pretrained("videoloc/seamless-crossattention", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29c29bac04057182c7498118e0907b44a33a08de0fb155f656bd98c8506487b2
- Size of remote file:
- 4.88 GB
- SHA256:
- 29764a5e44028038b2251c4bc21f8bccaefc03f06bd4e796f77683b4e7914e51
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