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 model_fp16.onnx_data from kitsunejb/colpali-qdrant: direct link, hf CLI and curl.
- Browser
- Download file 5.85 GB
-
https://huggingface.co/kitsunejb/colpali-qdrant/resolve/main/model_fp16.onnx_data
- Command line
-
hf download hf://kitsunejb/colpali-qdrant/model_fp16.onnx_data
-
curl -L -o model_fp16.onnx_data https://huggingface.co/kitsunejb/colpali-qdrant/resolve/main/model_fp16.onnx_data
5.85 GB
- Xet hash:
- 9bb55a1f8e844a4acbdb082c343731a9be5430bc2b5a05de74b2bb3fc7c5faf3
- Size of remote file:
- 5.85 GB
- SHA256:
- 38ed26c28572daef6277a507057f575d41c5693f4d3f8a3ada6e50d108986f86
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