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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("iamatif2003/Rubz") model = AutoModel.from_pretrained("iamatif2003/Rubz") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45be79340ab67b007fd80798eef0e039e52f64192b071d9c2d48e8ac57391921
- Size of remote file:
- 12.8 kB
- SHA256:
- 294a78873475f56da99dd15a19fcfa7d894d41efb108c7d7557a183400bb8d48
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.