Instructions to use Mahmoud8/bart-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/bart-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/bart-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/bart-large") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/bart-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahmoud8/bart-large: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Mahmoud8/bart-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahmoud8/bart-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahmoud8/bart-large/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 21ed019b63dccadb27ec5acfe9b00cb237f831ecd3d29ad0462dcb23ef11ce54
- Size of remote file:
- 268 MB
- SHA256:
- a7ab7d82bec9a1e2dc4570a67362928c0b337d5af57dad68fc170e617058b078
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.