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.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.05 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/18a1b958d1b0485d4687f79c7f2d9b9467787158/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@18a1b958d1b0485d4687f79c7f2d9b9467787158/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/18a1b958d1b0485d4687f79c7f2d9b9467787158/tokenized_train_datasetii.pt
4.05 GB
- Xet hash:
- 9179f6f2f4d43a11ef87e42dceb7d59fe166c8f0e0799183d6b3833a66e9f930
- Size of remote file:
- 4.05 GB
- SHA256:
- 89967bc722d3d31718d14c05a03434f4ef8b90c32a27179d62357c435e775a79
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