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 tokenizer.json from gsjang/kepri-embedding: direct link, hf CLI and curl.
- Browser
- Download file 752 kB
-
https://huggingface.co/gsjang/kepri-embedding/resolve/main/tokenizer.json
- Command line
-
hf download hf://gsjang/kepri-embedding/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/gsjang/kepri-embedding/resolve/main/tokenizer.json
752 kB
File too large to display, you can check the raw version instead.