Instructions to use pere/roberta-debug-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/roberta-debug-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pere/roberta-debug-32")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pere/roberta-debug-32") model = AutoModelForMaskedLM.from_pretrained("pere/roberta-debug-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from pere/roberta-debug-32: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/pere/roberta-debug-32/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pere/roberta-debug-32/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pere/roberta-debug-32/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- 53c9496daea194dd0db9ca5ce4da0f2be406eb7b72c864d8c306035f44b71a12
- Size of remote file:
- 1.11 GB
- SHA256:
- f19b32cd9e1957f954313f3992ccaa66b7536cc76ef182da6cb45ed4fe9d24f2
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