Instructions to use Prem11100/Clustering-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prem11100/Clustering-embeddings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Prem11100/Clustering-embeddings")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("Prem11100/Clustering-embeddings") model = AutoModel.from_pretrained("Prem11100/Clustering-embeddings", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Prem11100/Clustering-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Prem11100/Clustering-embeddings/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Prem11100/Clustering-embeddings/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Prem11100/Clustering-embeddings/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 6dd86fc89a69ec361784e0ab166d084f198f4895cd99f7ba161b15496948a467
- Size of remote file:
- 343 MB
- SHA256:
- d8e4db3960ed269a9e235611fb1607d4e7a25504225e0284f3a44871b4c1472e
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