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 3.27 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@670a35a7b351cdc5f26af908e9fea35bd5be76fe/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/tokenized_train_datasetii.pt
3.27 GB
- Xet hash:
- 503db833c73661a31d82f2fe43fee925b489e5229f8ab5092c7aabb411a9cb05
- Size of remote file:
- 3.27 GB
- SHA256:
- 87e3a8d28043ca02ddf91ec863c52f36e301c6185ef12d1bf22a821feccb0e45
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