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_datasetii.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.26 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/1bfa81b4dc8be645e43dabfbb8e7f8f3c84e412f/tokenized_train_datasetii.pt
- Command line
-
hf download hf://dilip025/dummy-model@1bfa81b4dc8be645e43dabfbb8e7f8f3c84e412f/tokenized_train_datasetii.pt
-
curl -L -o tokenized_train_datasetii.pt https://huggingface.co/dilip025/dummy-model/resolve/1bfa81b4dc8be645e43dabfbb8e7f8f3c84e412f/tokenized_train_datasetii.pt
3.26 GB
- Xet hash:
- bb1aea78b75e1dcb8275c0970768821a7138da164a4ec161e104068bec3ef847
- Size of remote file:
- 3.26 GB
- SHA256:
- 665de2f910f213fd81d90c31f49a4c5bd5cbca487939eaae032f261adebdeff5
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