Instructions to use Barkavi/bart-totto-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Barkavi/bart-totto-checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Barkavi/bart-totto-checkpoints") model = AutoModelForSeq2SeqLM.from_pretrained("Barkavi/bart-totto-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Barkavi/bart-totto-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/Barkavi/bart-totto-checkpoints/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Barkavi/bart-totto-checkpoints/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Barkavi/bart-totto-checkpoints/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- a5c47acdbdf97cb45476f99ef8423c553993ff87081fb7b97b593feb429497cf
- Size of remote file:
- 558 MB
- SHA256:
- ee1d04aea6d063d3ed650a15f76a3fd746916d48e13a39d9e9ee3beaa92fa400
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