Instructions to use dxkrnn/blipball with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dxkrnn/blipball with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("dxkrnn/blipball") model = AutoModelForMultimodalLM.from_pretrained("dxkrnn/blipball", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from dxkrnn/blipball: direct link, hf CLI and curl.
- Browser
- Download file 430 Bytes
-
https://huggingface.co/dxkrnn/blipball/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://dxkrnn/blipball/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/dxkrnn/blipball/resolve/main/preprocessor_config.json
430 Bytes
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "BlipImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "processor_class": "BlipProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| } | |
| } |