Instructions to use dilip025/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilip025/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilip025/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilip025/dummy-model") model = AutoModelForMaskedLM.from_pretrained("dilip025/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenized_train_datasetii.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.24 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/21f65e3a9e7502ebaa2952b5dc0d045550cef245/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@21f65e3a9e7502ebaa2952b5dc0d045550cef245/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/21f65e3a9e7502ebaa2952b5dc0d045550cef245/tokenized_train_datasetii.pt
3.24 GB
- Xet hash:
- 2f9e382ff4f25f5e9a287fbf942df9661294664bffadc47beeee226557ecdf9f
- Size of remote file:
- 3.24 GB
- SHA256:
- c8dca85d11120a9b4512b7e38f118093c710127ed3bce304ec2810ac980c0589
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