Instructions to use studio-ousia/luke-large-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/luke-large-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/luke-large-lite")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-lite") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/luke-large-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from studio-ousia/luke-large-lite: direct link, hf CLI and curl.
- Browser
- Download file 1.73 GB
-
https://huggingface.co/studio-ousia/luke-large-lite/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://studio-ousia/luke-large-lite/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/studio-ousia/luke-large-lite/resolve/main/pytorch_model.bin
1.73 GB
- Xet hash:
- 0831229b636031dbf0454c9f7f0692207f0ffab8439de0aae3601ca493b2715a
- Size of remote file:
- 1.73 GB
- SHA256:
- 3859e81cfafed6881b533045d058c6be5563defabc15a503e9e2217358d04d16
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