Instructions to use ProbeX/Model-J__MAE__model_idx_0514 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__MAE__model_idx_0514 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__MAE__model_idx_0514") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__MAE__model_idx_0514") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0514", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__MAE__model_idx_0514: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/ProbeX/Model-J__MAE__model_idx_0514/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__MAE__model_idx_0514/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__MAE__model_idx_0514/resolve/main/model.safetensors
343 MB
- Xet hash:
- fa5b4e6f95135b4a3f7e0f21ca7de9db530d8371dc658688d63e2fbc4bfff8a8
- Size of remote file:
- 343 MB
- SHA256:
- c05681e08043d2b61a8c6dbf0838fbea0f72a6e87a95bcd683bbff536500b23d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.