Instructions to use pere/roberta-base-exp-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/roberta-base-exp-32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pere/roberta-base-exp-32B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pere/roberta-base-exp-32B") model = AutoModelForMaskedLM.from_pretrained("pere/roberta-base-exp-32B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download copy_tokenizer_config.py from pere/roberta-base-exp-32B: direct link, hf CLI and curl.
- Browser
- Download file 312 Bytes
-
https://huggingface.co/pere/roberta-base-exp-32B/resolve/main/copy_tokenizer_config.py
- Command line
-
hf download hf://pere/roberta-base-exp-32B/copy_tokenizer_config.py
-
curl -L -o copy_tokenizer_config.py https://huggingface.co/pere/roberta-base-exp-32B/resolve/main/copy_tokenizer_config.py
312 Bytes
| # Code used for copying tokenizer and config | |
| from transformers import XLMRobertaTokenizerFast, XLMRobertaConfig | |
| tokenizer = XLMRobertaTokenizerFast.from_pretrained("xlm-roberta-base") | |
| config = XLMRobertaConfig.from_pretrained("xlm-roberta-base") | |
| tokenizer.save_pretrained("./") | |
| config.save_pretrained("./") | |