Instructions to use JulianPJ/BERT_BART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JulianPJ/BERT_BART with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JulianPJ/BERT_BART") model = AutoModelForSeq2SeqLM.from_pretrained("JulianPJ/BERT_BART", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JulianPJ/BERT_BART: direct link, hf CLI and curl.
- Browser
- Download file 1.02 GB
-
https://huggingface.co/JulianPJ/BERT_BART/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JulianPJ/BERT_BART/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JulianPJ/BERT_BART/resolve/main/pytorch_model.bin
1.02 GB
- Xet hash:
- 80a30d433e5bca2301ac5922187df46e03c43a282f6ca57695234694062e8852
- Size of remote file:
- 1.02 GB
- SHA256:
- e89801f30d0b0a07fb148a7c2f2b421b891445f2108c22f45de6c756982e28a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.