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_datasetii_test.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.89 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/f77a10a7965b855555a70642b46c6d579d4e17e4/tokenized_train_datasetii_test.pt
- Command line
-
hf download hf://dilip025/dummy-model@f77a10a7965b855555a70642b46c6d579d4e17e4/tokenized_train_datasetii_test.pt
-
curl -L -o tokenized_train_datasetii_test.pt https://huggingface.co/dilip025/dummy-model/resolve/f77a10a7965b855555a70642b46c6d579d4e17e4/tokenized_train_datasetii_test.pt
3.89 GB
- Xet hash:
- 79c701fc836045a4c975c7570f1b00a5d54b26eccdd2b6dda32987349d1b834f
- Size of remote file:
- 3.89 GB
- SHA256:
- 642b300e484dad4f92cdd24a1a0a3fb1ff406ca00f8970f876b6a5f2140ef0b4
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