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_datasetii3.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.02 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/tokenized_train_datasetii3.pt
- Command line
-
hf download hf://dilip025/dummy-model@8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/tokenized_train_datasetii3.pt
-
curl -L -o tokenized_train_datasetii3.pt https://huggingface.co/dilip025/dummy-model/resolve/8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/tokenized_train_datasetii3.pt
1.02 GB
- Xet hash:
- bf31f981d46c4e101518abb3b5f505ba2970173371093880d58c664d532df370
- Size of remote file:
- 1.02 GB
- SHA256:
- f3f8ebe1351cb4600f0e2d7d63819f5d9095dd43704c661f8b70f7bccab2002a
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