Instructions to use Junlaii/bart_4acticle_abstract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Junlaii/bart_4acticle_abstract with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Junlaii/bart_4acticle_abstract") model = AutoModelForSeq2SeqLM.from_pretrained("Junlaii/bart_4acticle_abstract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Junlaii/bart_4acticle_abstract: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/Junlaii/bart_4acticle_abstract/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Junlaii/bart_4acticle_abstract/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Junlaii/bart_4acticle_abstract/resolve/main/flax_model.msgpack
1.63 GB
- Xet hash:
- 4a3a4add8f1fccc7e452b893c8399355f4342b50533b66d869a132a27d2aa104
- Size of remote file:
- 1.63 GB
- SHA256:
- dda1068890b2e54a98369501ceebbed19edfe184a45581c1ecd228e3f224a4bf
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