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 3.9 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/74c5d58574f0b162d61db5e70069a0a0333efda7/tokenized_train_datasetii_test.pt
- Command line
-
hf download hf://dilip025/dummy-model@74c5d58574f0b162d61db5e70069a0a0333efda7/tokenized_train_datasetii_test.pt
-
curl -L -o tokenized_train_datasetii_test.pt https://huggingface.co/dilip025/dummy-model/resolve/74c5d58574f0b162d61db5e70069a0a0333efda7/tokenized_train_datasetii_test.pt
3.9 GB
- Xet hash:
- 250d38c9a754347df782d0cdb356954b485446898b0bcdd412998c680ea289e7
- Size of remote file:
- 3.9 GB
- SHA256:
- 7d84ca6e5fc189ceefffbcd508d466170d84c83840e7601be829cf5a556b4b2a
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