Instructions to use hf-tiny-model-private/tiny-random-BitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-BitModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-BitModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-BitModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-BitModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-tiny-model-private/tiny-random-BitModel: direct link, hf CLI and curl.
- Browser
- Download file 424 Bytes
-
https://huggingface.co/hf-tiny-model-private/tiny-random-BitModel/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-BitModel/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-tiny-model-private/tiny-random-BitModel/resolve/main/preprocessor_config.json
424 Bytes
| { | |
| "crop_size": { | |
| "height": 448, | |
| "width": 448 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "BitImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 448 | |
| } | |
| } | |