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")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("gsjang/kepri-embedding") model = AutoModelForMultimodalLM.from_pretrained("gsjang/kepri-embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 236006eedf87559e7643294ff2b4be8aa231387be7201025fd6e742a1e590143
- Size of remote file:
- 31.8 kB
- SHA256:
- f4802da0698b467591672e8edc185b3106b923dfcd820f6940b3a716a20cdf29
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