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 4.06 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@7837c07ea958e6aabde620fbf35619d8fe048737/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/tokenized_train_datasetii.pt
4.06 GB
- Xet hash:
- 29037e2d3dbd7355b3bd4e23e0affedf9bad9ca118249e15f585851960497b5d
- Size of remote file:
- 4.06 GB
- SHA256:
- 7457c3a2643659138a365643b2cff7c907e0ba858d9a7e12f56efe621358dedf
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