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_datasetii1.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.27 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/a7a00de86dc371e26e7bf7e16a2b76b6a91c6337/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@a7a00de86dc371e26e7bf7e16a2b76b6a91c6337/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/a7a00de86dc371e26e7bf7e16a2b76b6a91c6337/tokenized_train_datasetii1.pt
3.27 GB
- Xet hash:
- 40e4a2c2c7d1bfc86255edfeeeb0ea6d61c55f53c52cdf528592f1b5dfa65141
- Size of remote file:
- 3.27 GB
- SHA256:
- c5e85cf4753d275775ed36c3cc5aba721c103da79c2fb0eff04e6d534bd9d073
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