Instructions to use gowitheflowlab/clip-base-10240-checkpoint350 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gowitheflowlab/clip-base-10240-checkpoint350 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="gowitheflowlab/clip-base-10240-checkpoint350") 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("gowitheflowlab/clip-base-10240-checkpoint350") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-10240-checkpoint350", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from gowitheflowlab/clip-base-10240-checkpoint350: direct link, hf CLI and curl.
- Browser
- Download file 690 MB
-
https://huggingface.co/gowitheflowlab/clip-base-10240-checkpoint350/resolve/main/optimizer.pt
- Command line
-
hf download hf://gowitheflowlab/clip-base-10240-checkpoint350/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/gowitheflowlab/clip-base-10240-checkpoint350/resolve/main/optimizer.pt
690 MB
- Xet hash:
- 0044499307682ef4e5b0e4199292564fee3ca6e4bd782f262f18bf652eee1df2
- Size of remote file:
- 690 MB
- SHA256:
- 7081480710c794cb5441648480f3591ae54d2779636f351e34466a3eee9edefb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.