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 3.25 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/d650a7fe3bb8d15ef289c3bda99685ca39704ff8/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@d650a7fe3bb8d15ef289c3bda99685ca39704ff8/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/d650a7fe3bb8d15ef289c3bda99685ca39704ff8/tokenized_train_datasetii.pt
3.25 GB
- Xet hash:
- 8d2ab1f5e1b94cb2345f12a19aab82eb26dc4a5b15b82d295ebbdbc733c02641
- Size of remote file:
- 3.25 GB
- SHA256:
- a978f45b6554fa15fb6d1e4d123879e30fce7acd6ca3386ea3cfdb85f2edb566
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