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_datasetii1.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.26 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/f3899c831b94ba495b218f2f6391c07b0e87ce7c/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@f3899c831b94ba495b218f2f6391c07b0e87ce7c/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/f3899c831b94ba495b218f2f6391c07b0e87ce7c/tokenized_train_datasetii1.pt
3.26 GB
- Xet hash:
- 5fcff3e2b0b45e6c468fc23c43d75b1a1bd054647edbdd8168b4072ff1ebc97a
- Size of remote file:
- 3.26 GB
- SHA256:
- 38d0d719e7979662193ce6457eb05084d56500e7bc6f8c0c3ea1ee539e719a5e
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