Instructions to use rbawden/CCASS-semi-auto-titrages-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rbawden/CCASS-semi-auto-titrages-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rbawden/CCASS-semi-auto-titrages-base") model = AutoModelForSeq2SeqLM.from_pretrained("rbawden/CCASS-semi-auto-titrages-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rbawden/CCASS-semi-auto-titrages-base: direct link, hf CLI and curl.
- Browser
- Download file 273 MB
-
https://huggingface.co/rbawden/CCASS-semi-auto-titrages-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rbawden/CCASS-semi-auto-titrages-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rbawden/CCASS-semi-auto-titrages-base/resolve/main/pytorch_model.bin
273 MB
- Xet hash:
- b6dd4e553e8321ea37a7d96bda713b6ee686c068ca6511fbd4ad22fd0ad537b2
- Size of remote file:
- 273 MB
- SHA256:
- 07b483beb88637733b0077a65eb6a0d71ee61a3da47d2962bce2e48067c370b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.