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 3.25 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/21f65e3a9e7502ebaa2952b5dc0d045550cef245/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@21f65e3a9e7502ebaa2952b5dc0d045550cef245/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/21f65e3a9e7502ebaa2952b5dc0d045550cef245/tokenized_train_datasetii1.pt
3.25 GB
- Xet hash:
- 0237aced96b69746f4d98da031b66173923c5ea2ebae0bebcd27a4274fbf0ac7
- Size of remote file:
- 3.25 GB
- SHA256:
- 90508a205602f6e08a410f56887a80db6c8c94d0866911e5df0d80e11af1e9f2
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