Instructions to use igandarillas/supcon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igandarillas/supcon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="igandarillas/supcon")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("igandarillas/supcon") model = AutoModelForMaskedLM.from_pretrained("igandarillas/supcon", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from igandarillas/supcon: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/igandarillas/supcon/resolve/main/training_args.bin
- Command line
-
hf download hf://igandarillas/supcon/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/igandarillas/supcon/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 0a1f87adcce713f72fa594441f709452446d88f676e127dec87381cb721aa7c7
- Size of remote file:
- 3.31 kB
- SHA256:
- ebfee223d2fca02206e0537a3354f7e3524985d065f4f3059aec69c1a68011c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.