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 1.02 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/9e9bc3ffd08514b6fa9cfd4ea3e9d96cccae5ac9/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@9e9bc3ffd08514b6fa9cfd4ea3e9d96cccae5ac9/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/9e9bc3ffd08514b6fa9cfd4ea3e9d96cccae5ac9/tokenized_train_datasetii1.pt
1.02 GB
- Xet hash:
- 7f025bf990c59df872007645fc6e4dc5336b27cbb62d18ca7b8e799d66da762d
- Size of remote file:
- 1.02 GB
- SHA256:
- 1e8fdfef004a638e98698cf840099d2748c7aae2ce3e2736cfd04c1956cff8e7
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