Instructions to use Destux/distilbart-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Destux/distilbart-trained with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Destux/distilbart-trained") model = AutoModelForSeq2SeqLM.from_pretrained("Destux/distilbart-trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Destux/distilbart-trained: direct link, hf CLI and curl.
- Browser
- Download file 1.02 GB
-
https://huggingface.co/Destux/distilbart-trained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Destux/distilbart-trained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Destux/distilbart-trained/resolve/main/pytorch_model.bin
1.02 GB
- Xet hash:
- 79ba0c75c18556da3fa3c9eebe4db3bc8a5586146cab88a8799df2a6cd5c9960
- Size of remote file:
- 1.02 GB
- SHA256:
- 4a9a6a858a018f1132fa7437587b3649a3d79b7b550c0b142244b9eba52e9e81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.