Instructions to use hf-internal-testing/tiny-random-IdeficsModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-IdeficsModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-IdeficsModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-IdeficsModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-IdeficsModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-IdeficsModel: direct link, hf CLI and curl.
- Browser
- Download file 178 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-IdeficsModel/resolve/b873dfb2cd622425f9655f6c395abe048d431c4d/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-IdeficsModel@b873dfb2cd622425f9655f6c395abe048d431c4d/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-IdeficsModel/resolve/b873dfb2cd622425f9655f6c395abe048d431c4d/preprocessor_config.json
178 Bytes
| { | |
| "crop_size": 30, | |
| "image_mean": null, | |
| "image_num_channels": 3, | |
| "image_processor_type": "IdeficsImageProcessor", | |
| "image_size": 224, | |
| "image_std": null, | |
| "size": 30 | |
| } | |