Instructions to use xfact/FiD-NQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xfact/FiD-NQ with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, FiDT5 tokenizer = AutoTokenizer.from_pretrained("xfact/FiD-NQ") model = FiDT5.from_pretrained("xfact/FiD-NQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xfact/FiD-NQ: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/xfact/FiD-NQ/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xfact/FiD-NQ/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xfact/FiD-NQ/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- 47adca31a8d2402216971a23f2096aacaffcc461cd9109c34f46618ecca1467c
- Size of remote file:
- 892 MB
- SHA256:
- 86bc48c72cdd053767fa7ff692e3f10f278458425bb1015024a10e28cf0fc004
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