Instructions to use gowitheflowlab/clip-base-10240-checkpoint210 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gowitheflowlab/clip-base-10240-checkpoint210 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-checkpoint210") 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-checkpoint210") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-10240-checkpoint210", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from gowitheflowlab/clip-base-10240-checkpoint210: direct link, hf CLI and curl.
- Browser
- Download file 690 MB
-
https://huggingface.co/gowitheflowlab/clip-base-10240-checkpoint210/resolve/main/optimizer.pt
- Command line
-
hf download hf://gowitheflowlab/clip-base-10240-checkpoint210/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/gowitheflowlab/clip-base-10240-checkpoint210/resolve/main/optimizer.pt
690 MB
- Xet hash:
- 4dc4832c896e6ede65ce154a775401bd30d8344456659d85b32925b3777a1794
- Size of remote file:
- 690 MB
- SHA256:
- c004851ea191323643e26d01eacd048a8912cec18e4588c09cbb58c8781ea798
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