Instructions to use sander-wood/clamp-small-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sander-wood/clamp-small-1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sander-wood/clamp-small-1024")# Load model directly from transformers import AutoTokenizer, CLaMP tokenizer = AutoTokenizer.from_pretrained("sander-wood/clamp-small-1024") model = CLaMP.from_pretrained("sander-wood/clamp-small-1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4b608df6e3fc09a463ed86ce158e449cce2928a4a63fcc9564dc22992efea26
- Size of remote file:
- 528 MB
- SHA256:
- 0a8044d715a92b3c16c55fa945ff26d615d0a32165c87987ab0d969ec27c8481
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