Instructions to use gsjang/kepri-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsjang/kepri-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gsjang/kepri-embedding")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gsjang/kepri-embedding") model = AutoModel.from_pretrained("gsjang/kepri-embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gsjang/kepri-embedding: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/gsjang/kepri-embedding/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gsjang/kepri-embedding/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gsjang/kepri-embedding/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 9e7834e3b19bae5b52d0f1c7f9da8055634ab9a314f58a233ac795436120236d
- Size of remote file:
- 443 MB
- SHA256:
- bbad31734f3289fb3b4a559c9fe471b2ce2c509b62fd9f14fa9856733f431097
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