Instructions to use hf-tiny-model-private/tiny-random-ImageGPTForImageClassification 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-ImageGPTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-ImageGPTForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ImageGPTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ImageGPTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa0a6c0eea0cb1971fc8c65d2923348a18ed16828803ce993599a7ed53345620
- Size of remote file:
- 5.59 MB
- SHA256:
- 83b0037b6fdb23476ec5507da0caf353c48cfdaace236a73510c1d4393d12e9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.