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 4.04 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/e8084cffcae8493e6515c8980a60453a0744bce1/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@e8084cffcae8493e6515c8980a60453a0744bce1/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/e8084cffcae8493e6515c8980a60453a0744bce1/tokenized_train_datasetii1.pt
4.04 GB
- Xet hash:
- 293e25c9467b78d75888ffc1107df2597b57e85acb1899d697db7a8651d71281
- Size of remote file:
- 4.04 GB
- SHA256:
- 9cf27b63b395b6fe45cf31f4e5c883a2925b38237764e4f492525941645fa890
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