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_datasetii2.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.04 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/520694c2680e5346fe915b66a46ad997bc188d69/tokenized_train_datasetii2.pt
- Command line
-
hf download hf://dilip025/dummy-model@520694c2680e5346fe915b66a46ad997bc188d69/tokenized_train_datasetii2.pt
-
curl -L -o tokenized_train_datasetii2.pt https://huggingface.co/dilip025/dummy-model/resolve/520694c2680e5346fe915b66a46ad997bc188d69/tokenized_train_datasetii2.pt
4.04 GB
- Xet hash:
- 75f5d84282e698402bd0bbc823c3574c0ad64a11a3853ab4c04f7602da1eb904
- Size of remote file:
- 4.04 GB
- SHA256:
- 789fc90ebec90fe5e2716d2951fb462e5f649ef08f003459233a4300b5dad1dc
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