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 3.27 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/cb400397f72773e09f128bbc30294f816738930c/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@cb400397f72773e09f128bbc30294f816738930c/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/cb400397f72773e09f128bbc30294f816738930c/tokenized_train_datasetii1.pt
3.27 GB
- Xet hash:
- dc8ab24e2290a8719583c007a5d6251c14e068e83bc619a62ab44336b67ad7d8
- Size of remote file:
- 3.27 GB
- SHA256:
- 8bafec806352bc9e99368c2efce0f3c3c4d389d38fac8f77e0f99262241515b1
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