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 pytorch_model.bin from facebook/bart-large: direct link, hf CLI and curl.
- Browser
- Download file 1.02 GB
-
https://huggingface.co/facebook/bart-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/bart-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/bart-large/resolve/main/pytorch_model.bin
1.02 GB
- Xet hash:
- 096d9d0bdbc17fb6ea215db711d39c5f8f3810c431fcb24765f138eeaa999d9e
- Size of remote file:
- 1.02 GB
- SHA256:
- 5e92f2a20e7735792c048457f6dd9c2ae03d4159738746bf6aab42389df99205
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.