Instructions to use iamatif2003/Rubz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iamatif2003/Rubz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="iamatif2003/Rubz", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("iamatif2003/Rubz") model = AutoModel.from_pretrained("iamatif2003/Rubz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1739859b75700da31bcba1c7e10e49934091bc71b2c9ce175efba91e5cb07d87
- Size of remote file:
- 29.8 MB
- SHA256:
- fd60685ccbac99d9caf272579e860da23534966fe41ded650d4ad2f8e144bc58
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