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")# pip install -U transformers accelerate # 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 410 MB
-
https://huggingface.co/dilip025/dummy-model/resolve/d1d4befd36458a06ff5ef58d3049e7cf9a11d6a9/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@d1d4befd36458a06ff5ef58d3049e7cf9a11d6a9/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/d1d4befd36458a06ff5ef58d3049e7cf9a11d6a9/tokenized_train_datasetii1.pt
410 MB
- Xet hash:
- 03711c1d2d9f45237bade889fa617ddcab6c735e59609c78a22029f55d8a6306
- Size of remote file:
- 410 MB
- SHA256:
- 08a7f04121f5b44730ef9da437653b48c26dba1778baa30855b1f8f1896307e6
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