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_datasetii1.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.04 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/a4c01fac080bc2a6cc3995bc84acbda834d0b130/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@a4c01fac080bc2a6cc3995bc84acbda834d0b130/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/a4c01fac080bc2a6cc3995bc84acbda834d0b130/tokenized_train_datasetii1.pt
4.04 GB
- Xet hash:
- 5eaeedbb817b5a50fa843b8ebb40c628b99cce87787e23dc53671efe779c945a
- Size of remote file:
- 4.04 GB
- SHA256:
- d6ddabb9568d230b265f71a13c45ecd870c018cf06351bbf2e73e1ebf51163c4
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