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_test.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.24 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/5456a3b7b8ae217e2edcbc3bdaacce7ebf228a39/tokenized_train_datasetii_test.pt
- Command line
-
hf download hf://dilip025/dummy-model@5456a3b7b8ae217e2edcbc3bdaacce7ebf228a39/tokenized_train_datasetii_test.pt
-
curl -L -o tokenized_train_datasetii_test.pt https://huggingface.co/dilip025/dummy-model/resolve/5456a3b7b8ae217e2edcbc3bdaacce7ebf228a39/tokenized_train_datasetii_test.pt
3.24 GB
- Xet hash:
- 7f6254b080f77aa662e5375514d58ffaddd6b4df0a4188d0af31f25d6fbfe5d1
- Size of remote file:
- 3.24 GB
- SHA256:
- b7eed2afba615ae0d2f6a44888f08f3c0913d7b1b39450b984c104ab837b54f5
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