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")# 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 flax_model.msgpack from facebook/bart-large: direct link, hf CLI and curl.
- Browser
- Download file 813 MB
-
https://huggingface.co/facebook/bart-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://facebook/bart-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/facebook/bart-large/resolve/main/flax_model.msgpack
813 MB
- Xet hash:
- 026720fd4d65229df663127dab6ced78fb230324041f3ba8ad0ef70bb23842db
- Size of remote file:
- 813 MB
- SHA256:
- 954a481dd580ea66a4c221ffc66486f2dc6850f8012e16428a4f8d45f67718f9
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