Instructions to use Angshul/SpliNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angshul/SpliNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Angshul/SpliNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Angshul/SpliNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 807 kB
-
https://huggingface.co/Angshul/SpliNet/resolve/main/spiece.model
- Command line
-
hf download hf://Angshul/SpliNet/spiece.model
-
curl -L -o spiece.model https://huggingface.co/Angshul/SpliNet/resolve/main/spiece.model
807 kB
- Xet hash:
- ccc9e2f0bce93790996093505129a691997c31e58aab6de44726105d82f98987
- Size of remote file:
- 807 kB
- SHA256:
- 119ec6b2af9cbbc56f297bd606b69f79f6e3130a34ee56122ee813a58d15bb9d
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