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 flax_model.msgpack from pere/roberta-base-exp-32B: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/pere/roberta-base-exp-32B/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pere/roberta-base-exp-32B/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pere/roberta-base-exp-32B/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- b6b4aa9be14fc1ae59580905fadd1d4899163a9c4374b94d879e6bd137928cb8
- Size of remote file:
- 1.11 GB
- SHA256:
- 4048451f49728794264a7ff3f3aeae000e26a2d99310600b149d9f8df5c7eba1
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