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 requirements.txt from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 67 Bytes
-
https://huggingface.co/Angshul/SpliNet/resolve/main/requirements.txt
- Command line
-
hf download hf://Angshul/SpliNet/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/Angshul/SpliNet/resolve/main/requirements.txt
67 Bytes
| torch | |
| transformers>=4.45.0 | |
| sentencepiece>=0.2.0 | |
| safetensors>=0.4.5 | |