Instructions to use kbnlresearch/spa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbnlresearch/spa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kbnlresearch/spa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kbnlresearch/spa") model = AutoModelForMaskedLM.from_pretrained("kbnlresearch/spa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kbnlresearch/spa: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/kbnlresearch/spa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kbnlresearch/spa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kbnlresearch/spa/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- b94380ffb6b5435b528b4f7d494f8663209b02e5166e876ee5293cee5b01b1dd
- Size of remote file:
- 712 MB
- SHA256:
- 89f148c4fb13fa4fd37041a058dedde4261b9f1bfca717e3ca8e4d4997037397
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