Instructions to use dill-lab/oath-frames-flant5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dill-lab/oath-frames-flant5-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dill-lab/oath-frames-flant5-base") model = AutoModelForSeq2SeqLM.from_pretrained("dill-lab/oath-frames-flant5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dill-lab/oath-frames-flant5-base: direct link, hf CLI and curl.
- Browser
- Download file 4.16 kB
-
https://huggingface.co/dill-lab/oath-frames-flant5-base/resolve/main/training_args.bin
- Command line
-
hf download hf://dill-lab/oath-frames-flant5-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dill-lab/oath-frames-flant5-base/resolve/main/training_args.bin
4.16 kB
- Xet hash:
- 5badc123d6e5d7494a670ad9c775b53e971d72a4894d0ce29656a7f99069b5a6
- Size of remote file:
- 4.16 kB
- SHA256:
- 6a65931c0b82d4be8a1a51b054dfa49ab148b5b436df554d6e430e286fc8340a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.