Instructions to use kitsunejb/colpali-qdrant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kitsunejb/colpali-qdrant with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("kitsunejb/colpali-qdrant") model = AutoModelForPreTraining.from_pretrained("kitsunejb/colpali-qdrant", device_map="auto") - ColPali
How to use kitsunejb/colpali-qdrant with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kitsunejb/colpali-qdrant: direct link, hf CLI and curl.
- Browser
- Download file 17 MB
-
https://huggingface.co/kitsunejb/colpali-qdrant/resolve/main/tokenizer.json
- Command line
-
hf download hf://kitsunejb/colpali-qdrant/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kitsunejb/colpali-qdrant/resolve/main/tokenizer.json
17 MB
- Xet hash:
- 2ffd34615b09a49c8966c224724e059b51343c1481fe565a662be1492adcbd0b
- Size of remote file:
- 17 MB
- SHA256:
- da2a07b519fc15ff0bbebb8c671c7802fad5012fef72f3d2f1b6d32057be694e
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