Instructions to use sshleifer/bart-large-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/bart-large-fp32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sshleifer/bart-large-fp32")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sshleifer/bart-large-fp32") model = AutoModel.from_pretrained("sshleifer/bart-large-fp32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f30aa44114c4883d91ddc13239b55eba10af8cc57bebc1a006f2920cfd995c7c
- Size of remote file:
- 1.63 GB
- SHA256:
- 52a4611abb0f9c7cc6accb96d040b7d0cd83f1b907de4fa004c277942da80454
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