Instructions to use gubartz/sparse_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gubartz/sparse_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gubartz/sparse_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gubartz/sparse_model") model = AutoModelForMaskedLM.from_pretrained("gubartz/sparse_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gubartz/sparse_model: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/gubartz/sparse_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://gubartz/sparse_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gubartz/sparse_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
440 MB
- Xet hash:
- 88e3c55559957bb0ee701db4a096053f0b5440dd7a3ccc97f235a28c9d8e928e
- Size of remote file:
- 440 MB
- SHA256:
- 8c55e491124aef4ad80194c475c355f320bb080ceba45e36091db6bd34a305e2
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