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 4.04 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@ed10108ac2b9d53434695278f66eaffe49f977af/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/tokenized_train_datasetii1.pt
4.04 GB
- Xet hash:
- 9946b6d45901c10c5841b0c8083884540d6ca3fe0a9f61e1a4738575fef5af8c
- Size of remote file:
- 4.04 GB
- SHA256:
- e585e0bfa16935a325b97c1bc2a244bbfe1dbbd7baa614de18b5fcd6c8671db3
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