Instructions to use google/fnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/fnet-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("google/fnet-base") model = AutoModelForPreTraining.from_pretrained("google/fnet-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from google/fnet-base: direct link, hf CLI and curl.
- Browser
- Download file 708 kB
-
https://huggingface.co/google/fnet-base/resolve/main/spiece.model
- Command line
-
hf download hf://google/fnet-base/spiece.model
-
curl -L -o spiece.model https://huggingface.co/google/fnet-base/resolve/main/spiece.model
708 kB
- Xet hash:
- eb2b30775dd156065295f8a78e15631da54bf2f59866ccfba0f9ef7ddcc9e136
- Size of remote file:
- 708 kB
- SHA256:
- 0f848afb4cb35389f15819aad6b9b4bb65d8b6cec9613b6145bea94b4a045ab6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.