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 model.safetensors from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 332 MB
-
https://huggingface.co/Angshul/SpliNet/resolve/main/model.safetensors
- Command line
-
hf download hf://Angshul/SpliNet/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Angshul/SpliNet/resolve/main/model.safetensors
332 MB
- Xet hash:
- f5ca183f3f4cf108b9ae3659b178b2dfdb7a5640b28cd4c9283466d41bf6f49c
- Size of remote file:
- 332 MB
- SHA256:
- a891f4a978c2551fc8851346601fa3d560701ba69471272c4c5a39f4d70fbdd6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.