Instructions to use facebook/bart-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/bart-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/bart-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large") model = AutoModel.from_pretrained("facebook/bart-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.onnx from facebook/bart-large: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/facebook/bart-large/resolve/refs%2Fpr%2F5/model.onnx
- Command line
-
hf download hf://facebook/bart-large@refs/pr/5/model.onnx
-
curl -L -o model.onnx https://huggingface.co/facebook/bart-large/resolve/refs%2Fpr%2F5/model.onnx
1.63 GB
- Xet hash:
- 93a9fe51c8e2d277bcf1596921fbb7ff719dfedeed1d2eb77eabde1ba4b1cde7
- Size of remote file:
- 1.63 GB
- SHA256:
- 3f72650b6bcabd4f59e55603c956677b64653a1241559a9b55dda60ad817f115
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.