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 checksums.sha256 from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 1.1 kB
-
https://huggingface.co/Angshul/SpliNet/resolve/main/checksums.sha256
- Command line
-
hf download hf://Angshul/SpliNet/checksums.sha256
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curl -L -o checksums.sha256 https://huggingface.co/Angshul/SpliNet/resolve/main/checksums.sha256
1.1 kB
| 2d50d51e32c691a1034ed87770298f054aaf7fcf6e3046274a3b528f3d0135d0 README.md | |
| e22e250a1383a774e2842c1f9183895ea7678b6974114489aec8752b06f4dcb8 config.json | |
| 91c0167660e96c39d55a31053c58f87abbcbe58cf22525dfb9b90ce6d28bdd3e configuration_splinet.py | |
| a891f4a978c2551fc8851346601fa3d560701ba69471272c4c5a39f4d70fbdd6 model.safetensors | |
| 68b78991d599f1c6c3b52d3882bb07e65c81243500064b9317e0f289f57576ba modeling_splinet.py | |
| 321813b734e7c50df348e53600d5b90ba9d204eba535545279babac85babe2ab requirements.txt | |
| ace55eb0f41e170e8f3c891dd4ab707a2d871299ab7e4f4116c6a9d3128f31cf special_tokens_map.json | |
| 119ec6b2af9cbbc56f297bd606b69f79f6e3130a34ee56122ee813a58d15bb9d spiece.model | |
| ec799958399f5fcd35f12ac451e6b7517e2eda01f50a7b9d2089c1425e9a0d05 spiece.vocab | |
| 9e0b42c275c87f712d095f002bbfa5a435ed89250785ef9b4d4ee9a7ab18a53e tokenization_splinet.py | |
| 8171bc64afff9fe2f89ac1e011d0b829d4b5413b398ef7b8b9f408ad5cd30325 tokenizer_config.json | |
| d80f0c668bf183e6f327c167bb2723f0e565c946c05d6b5587afec7ca10575fb tokenizer_metadata.json | |
| 8f64420de4f4a6cd9ec7c8920f8be32463c8ba32d85dc9ab86865c3aba01e804 training_metadata.json | |