Instructions to use Jeevesh8/sMLM-256-LF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/sMLM-256-LF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jeevesh8/sMLM-256-LF")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/sMLM-256-LF") model = AutoModelForMaskedLM.from_pretrained("Jeevesh8/sMLM-256-LF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a24f5fc2c76ecfd18fa53cb08eb0c28406debeaf773cba19a51fe4625673d47e
- Size of remote file:
- 595 MB
- SHA256:
- 88f2dfb08b5ab31efa34c60ee52709d99b8daa42fd3f480650c3901f3cc36f7b
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