Instructions to use Fuat55/brain_RMSprop_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fuat55/brain_RMSprop_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Fuat55/brain_RMSprop_model") 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("Fuat55/brain_RMSprop_model") model = AutoModelForImageClassification.from_pretrained("Fuat55/brain_RMSprop_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Fuat55/brain_RMSprop_model: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/Fuat55/brain_RMSprop_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Fuat55/brain_RMSprop_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Fuat55/brain_RMSprop_model/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 95f64d4bf95fd3fcaeac3db5e61b3ea6542996e8710fcdf06c229235ad797684
- Size of remote file:
- 4.6 kB
- SHA256:
- 6ba0390288aa3238d48fce4955bb5a8fa03eca7fbb62f562eb8885c117c20b7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.