Instructions to use Arjav/TOS-Longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arjav/TOS-Longformer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Arjav/TOS-Longformer") model = AutoModelForSeq2SeqLM.from_pretrained("Arjav/TOS-Longformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Arjav/TOS-Longformer: direct link, hf CLI and curl.
- Browser
- Download file 1.84 GB
-
https://huggingface.co/Arjav/TOS-Longformer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Arjav/TOS-Longformer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Arjav/TOS-Longformer/resolve/main/pytorch_model.bin
1.84 GB
- Xet hash:
- 31904ada73ca60924b0ff66174b0dbbc62b6afc4eab5e55e05a955cc0571d333
- Size of remote file:
- 1.84 GB
- SHA256:
- 79b81af62a163fa1e85035940fbee34e9678e8c83fd65a477b2c4c85f8af0ca1
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