Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use CollectionStudio/tipsv2-g14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/tipsv2-g14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="CollectionStudio/tipsv2-g14", trust_remote_code=True) 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, AutoModel processor = AutoProcessor.from_pretrained("CollectionStudio/tipsv2-g14", trust_remote_code=True) model = AutoModel.from_pretrained("CollectionStudio/tipsv2-g14", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from CollectionStudio/tipsv2-g14: direct link, hf CLI and curl.
- Browser
- Download file 6.1 GB
-
https://huggingface.co/CollectionStudio/tipsv2-g14/resolve/main/model.safetensors
- Command line
-
hf download hf://CollectionStudio/tipsv2-g14/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/CollectionStudio/tipsv2-g14/resolve/main/model.safetensors
6.1 GB
- Xet hash:
- 34cf2422e5fab63ee8fb2af8e5c165f7e456f0cd48aae73d19f17996fe4ae406
- Size of remote file:
- 6.1 GB
- SHA256:
- c31c27837b177bbb6cc4d7234a01476e357fdb0bbd9d6584b4c5b0dd9b4ebfe6
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