Instructions to use Amna100/Fold-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amna100/Fold-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Amna100/Fold-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Amna100/Fold-2") model = AutoModelForTokenClassification.from_pretrained("Amna100/Fold-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Amna100/Fold-2: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Amna100/Fold-2/resolve/206f5c29e68eb2073ea9ebc520ad435753f85075/training_args.bin
- Command line
-
hf download hf://Amna100/Fold-2@206f5c29e68eb2073ea9ebc520ad435753f85075/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Amna100/Fold-2/resolve/206f5c29e68eb2073ea9ebc520ad435753f85075/training_args.bin
5.11 kB
- Xet hash:
- 4c33676a7a165d7b3e9a5087976835d41e1f5315dab9d206fe742adb9697ccd4
- Size of remote file:
- 5.11 kB
- SHA256:
- abe6ae7788ffdf0d120149a973d9d0cf9277c24cf80b47c4165d846835007ddf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.