Instructions to use k-l-lambda/clip-text-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k-l-lambda/clip-text-generator with Transformers:
# Load model directly from transformers import ClipTextGenerator model = ClipTextGenerator.from_pretrained("k-l-lambda/clip-text-generator", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37d72f7171b454005b569c0c275c24edfe5f69e7686b85047bccd958701c86e8
- Size of remote file:
- 644 MB
- SHA256:
- 3ad9ddde605b37c80a3c202200e728fcb0ffb4e9422635cf0cdf46eb2fa15f22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.