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 preprocessor_config.json from kitsunejb/colpali-qdrant: direct link, hf CLI and curl.
- Browser
- Download file 423 Bytes
-
https://huggingface.co/kitsunejb/colpali-qdrant/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://kitsunejb/colpali-qdrant/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/kitsunejb/colpali-qdrant/resolve/main/preprocessor_config.json
423 Bytes
| { | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_seq_length": 1024, | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "ColPaliProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 448, | |
| "width": 448 | |
| } | |
| } | |