Instructions to use pere/robertabasescandi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/robertabasescandi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pere/robertabasescandi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pere/robertabasescandi") model = AutoModelForMaskedLM.from_pretrained("pere/robertabasescandi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from pere/robertabasescandi: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/pere/robertabasescandi/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pere/robertabasescandi/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pere/robertabasescandi/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- ce60f0913e626c982c1e94e295c2bfcdac424958e356a8cc619c1f2ef1de4bcb
- Size of remote file:
- 1.11 GB
- SHA256:
- 660773cb12c04bf1245fce9624d5d136e2ebdfa9edae99529e716bbc3341c5f7
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