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 pytorch_model.bin from dill-lab/oath-frames-flant5-base: direct link, hf CLI and curl.
- Browser
- Download file 990 MB
-
https://huggingface.co/dill-lab/oath-frames-flant5-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dill-lab/oath-frames-flant5-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dill-lab/oath-frames-flant5-base/resolve/main/pytorch_model.bin
990 MB
- Xet hash:
- b5aa6726d15eedc2111dc5830f62b933b039f7bc1b7f3ea3170bab8e00c3c080
- Size of remote file:
- 990 MB
- SHA256:
- 56c4d79ae27f0da2001619e8df86af95604c693a44dbc208425ff15d6ceb365b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.