Instructions to use cahya/bart-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/bart-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cahya/bart-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cahya/bart-large") model = AutoModel.from_pretrained("cahya/bart-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from cahya/bart-large: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/cahya/bart-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://cahya/bart-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/cahya/bart-large/resolve/main/flax_model.msgpack
1.63 GB
- Xet hash:
- 4878955734e8ae7b3272645ee4f7559d043267303a51a1682f59bdb9af36bcb8
- Size of remote file:
- 1.63 GB
- SHA256:
- 56728b8d05b1bbf7474f0db82c49a3cb4397e270e0f5148582c749d179cf2537
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