Instructions to use hf-tiny-model-private/tiny-random-AltCLIPModel 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-AltCLIPModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-tiny-model-private/tiny-random-AltCLIPModel") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-AltCLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-AltCLIPModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-AltCLIPModel: direct link, hf CLI and curl.
- Browser
- Download file 586 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-AltCLIPModel/resolve/8251bf655999b87ecce4d64f64457f27820d4dc0/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-AltCLIPModel@8251bf655999b87ecce4d64f64457f27820d4dc0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-AltCLIPModel/resolve/8251bf655999b87ecce4d64f64457f27820d4dc0/pytorch_model.bin
586 kB
- Xet hash:
- ec3d8ac6fec928cc120ba4e5ea4e3cf403ece6dd5640965f21451495c9b6a8b0
- Size of remote file:
- 586 kB
- SHA256:
- 02c4109fb8ee31f8c0a061561fad9363d5961766d2956422294832695f5ed5d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.