Instructions to use miittnnss/idk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miittnnss/idk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="miittnnss/idk") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("miittnnss/idk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from miittnnss/idk: direct link, hf CLI and curl.
- Browser
- Download file 302 Bytes
-
https://huggingface.co/miittnnss/idk/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://miittnnss/idk@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/miittnnss/idk/resolve/refs%2Fpr%2F1/preprocessor_config.json
302 Bytes
- Xet hash:
- 5003a7deda1d317760481f067f648ec03e0691bda14ca6a85996e5bb910ff138
- Size of remote file:
- 302 Bytes
- SHA256:
- e04b5163749dcc966092f2dd359d88ed499f1ed5d3d8b1095d9b9d7f54b1c8be
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