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 3.26 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/68f09f322d2184ee1423220fe6eb6a1d295e3e73/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@68f09f322d2184ee1423220fe6eb6a1d295e3e73/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/68f09f322d2184ee1423220fe6eb6a1d295e3e73/tokenized_train_datasetii1.pt
3.26 GB
- Xet hash:
- 61ae5de583cb337ade97cdfd7d68addf402b134b88cc7270061bd1e6a51a4090
- Size of remote file:
- 3.26 GB
- SHA256:
- 9b2887f7f75fa04c92a53cf68049cfcf5ac709196246277549874f757236c66e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.