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_datasetii.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.22 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/5e6b8a1a7c591b21d92926db924b01acc903cab0/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@5e6b8a1a7c591b21d92926db924b01acc903cab0/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/5e6b8a1a7c591b21d92926db924b01acc903cab0/tokenized_train_datasetii.pt
3.22 GB
- Xet hash:
- d331a2ccba055da806e076de0142a909f67978328ae09a16d56216e12f82e358
- Size of remote file:
- 3.22 GB
- SHA256:
- bded3c5585d8f041b1d9b81a573aee29926d1196a694943a5d0d7e9180c3d636
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