Instructions to use gszabo/base_subtoken20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gszabo/base_subtoken20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gszabo/base_subtoken20")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gszabo/base_subtoken20") model = AutoModelForMaskedLM.from_pretrained("gszabo/base_subtoken20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gszabo/base_subtoken20: direct link, hf CLI and curl.
- Browser
- Download file 406 MB
-
https://huggingface.co/gszabo/base_subtoken20/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gszabo/base_subtoken20/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gszabo/base_subtoken20/resolve/main/pytorch_model.bin
406 MB
- Xet hash:
- 48aa943c53048ed42f374bedcc4ae70361af9e870bfc1e6e54f8e4236d6dd4f3
- Size of remote file:
- 406 MB
- SHA256:
- 4cd6ea4e1038209a2c7b0b276780511ea13dd040108fbbc6852e0cd96c62a757
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.