Instructions to use CataME/tp_nlp_Ruperta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CataME/tp_nlp_Ruperta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CataME/tp_nlp_Ruperta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("CataME/tp_nlp_Ruperta") model = AutoModel.from_pretrained("CataME/tp_nlp_Ruperta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CataME/tp_nlp_Ruperta: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/CataME/tp_nlp_Ruperta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CataME/tp_nlp_Ruperta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CataME/tp_nlp_Ruperta/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 713a3cf5b94cae294dc4fc41605aa03e1e0d50b19fccf37b5a9455c8bb775d43
- Size of remote file:
- 504 MB
- SHA256:
- 3b4cbba4a87ef4d02ecc7af9c669a7f1e3f34d28e53dc3922aff955c3b643f45
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