Instructions to use Bingsu/vitB32_bert_ko_small_clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/vitB32_bert_ko_small_clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Bingsu/vitB32_bert_ko_small_clip")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Bingsu/vitB32_bert_ko_small_clip") model = AutoModel.from_pretrained("Bingsu/vitB32_bert_ko_small_clip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Bingsu/vitB32_bert_ko_small_clip: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Bingsu/vitB32_bert_ko_small_clip/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Bingsu/vitB32_bert_ko_small_clip/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Bingsu/vitB32_bert_ko_small_clip/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- c8e94359ff734be8e8934eca6dc6258e45dcc9f76da70eb00122d4afabd1f94c
- Size of remote file:
- 443 MB
- SHA256:
- 0f9fd5f573fa3c1acfa2bcef605f290a2748141dbc7566d6c2943bc87c0e7134
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