Instructions to use summervent/speller-example_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use summervent/speller-example_ with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("summervent/speller-example_") model = AutoModelForSeq2SeqLM.from_pretrained("summervent/speller-example_", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from summervent/speller-example_: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/summervent/speller-example_/resolve/main/training_args.bin
- Command line
-
hf download hf://summervent/speller-example_/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/summervent/speller-example_/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- b62be6f05543e6ae0b528f02eb2ab517d4d5aeb96c215f370056f41909f7d052
- Size of remote file:
- 3.58 kB
- SHA256:
- 93cfca22ef4e2f4bbada5a629acc499e329758a93658f3504012704625694751
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.