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_datasetii_test.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 319 MB
-
https://huggingface.co/dilip025/dummy-model/resolve/cd7dd85335e6035b7b5298b2ba1b18c44d4d3f62/tokenized_train_datasetii_test.pt
- Command line
-
hf download hf://dilip025/dummy-model@cd7dd85335e6035b7b5298b2ba1b18c44d4d3f62/tokenized_train_datasetii_test.pt
-
curl -L -o tokenized_train_datasetii_test.pt https://huggingface.co/dilip025/dummy-model/resolve/cd7dd85335e6035b7b5298b2ba1b18c44d4d3f62/tokenized_train_datasetii_test.pt
319 MB
- Xet hash:
- 6036c365de8a32c563c47cd6d9c97c99d901c9ed36336ce9972086519b2874f8
- Size of remote file:
- 319 MB
- SHA256:
- 01b9236d561909ae72e9fc2e00b81a7fbeb76d519e5f164d4bc7a11c55d068f1
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