Instructions to use RavenK/TAC-ViT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RavenK/TAC-ViT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RavenK/TAC-ViT-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RavenK/TAC-ViT-base") model = AutoModel.from_pretrained("RavenK/TAC-ViT-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ea3170c074cfcf8aeb5d7a934b9816f9ef02890ab54e97bbb9d0ab13e258914
- Size of remote file:
- 350 MB
- SHA256:
- 02ab36e9f517c51323b8bc0d1994a7716a262470b928ad38d6ffad2bbbc27735
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