Instructions to use caiocof/teste_bertimbau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use caiocof/teste_bertimbau with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="caiocof/teste_bertimbau")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("caiocof/teste_bertimbau") model = AutoModelForMaskedLM.from_pretrained("caiocof/teste_bertimbau", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from caiocof/teste_bertimbau: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/caiocof/teste_bertimbau/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://caiocof/teste_bertimbau/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/caiocof/teste_bertimbau/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 951968c06852e52b9ad2aaada7c023643818d585c45cfe6c432bd65ac294da93
- Size of remote file:
- 436 MB
- SHA256:
- c5e6e50a44790865e2af5990dda25cbac419aa809269d0dd7ca3caa5defbcc69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.