Instructions to use lots-o/ko-albert-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lots-o/ko-albert-large-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lots-o/ko-albert-large-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lots-o/ko-albert-large-v1") model = AutoModelForMaskedLM.from_pretrained("lots-o/ko-albert-large-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lots-o/ko-albert-large-v1: direct link, hf CLI and curl.
- Browser
- Download file 77.6 MB
-
https://huggingface.co/lots-o/ko-albert-large-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lots-o/ko-albert-large-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lots-o/ko-albert-large-v1/resolve/main/pytorch_model.bin
77.6 MB
- Xet hash:
- 579024eedc680aaa61dd1f244658ab2769f73d130406933768c29e73ceefe5a3
- Size of remote file:
- 77.6 MB
- SHA256:
- 8c48ece5fee4543d1c21f8418f0c55e802f5479e8d7df24d16d0078fa0cad64e
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