Instructions to use Kalindu/SinBerto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kalindu/SinBerto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kalindu/SinBerto")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kalindu/SinBerto") model = AutoModelForMaskedLM.from_pretrained("Kalindu/SinBerto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kalindu/SinBerto: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/Kalindu/SinBerto/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Kalindu/SinBerto/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kalindu/SinBerto/resolve/main/pytorch_model.bin
336 MB
- Xet hash:
- 9d1762ff21d86d4b21b612e5482de33f7e15c2c777b83b05260044771ab62af9
- Size of remote file:
- 336 MB
- SHA256:
- 32c2ffbd77e2567477450661d953f633fe1a969fc4836b5d1f1c9cbb2d73a04f
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