Instructions to use cook/cicero-similis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cook/cicero-similis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cook/cicero-similis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cook/cicero-similis") model = AutoModelForMaskedLM.from_pretrained("cook/cicero-similis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from cook/cicero-similis: direct link, hf CLI and curl.
- Browser
- Download file 251 MB
-
https://huggingface.co/cook/cicero-similis/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://cook/cicero-similis/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/cook/cicero-similis/resolve/main/flax_model.msgpack
251 MB
- Xet hash:
- 31b318805709a393ca2a90fbc148411ee4c810162a7f9d745fcbc9dfcc41e3c0
- Size of remote file:
- 251 MB
- SHA256:
- 0e6882aa3f3848125ba6bfd788f8dcbb1ee457d6f3302e05902f8caa5628f991
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.