Instructions to use pere/roberta-debug-32-pod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/roberta-debug-32-pod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pere/roberta-debug-32-pod")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pere/roberta-debug-32-pod") model = AutoModelForMaskedLM.from_pretrained("pere/roberta-debug-32-pod", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from pere/roberta-debug-32-pod: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/pere/roberta-debug-32-pod/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pere/roberta-debug-32-pod/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pere/roberta-debug-32-pod/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- 7162f1f762d6a7d85f9db7bb19e2e84f3a71597d23cc692fbc3158368ebaf913
- Size of remote file:
- 1.11 GB
- SHA256:
- 86694e6fadbfded78642f0c6edb5c6d405b6dd68a5b5dcd57759bb55f1e6750d
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