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 ir_checkpoints/step_lightning_12500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/d8269403a2597ca8b68cc65bafd2aabc3cce4fb8/ir_checkpoints/step_lightning_12500.pt
- Command line
-
hf download hf://dilip025/dummy-model@d8269403a2597ca8b68cc65bafd2aabc3cce4fb8/ir_checkpoints/step_lightning_12500.pt
-
curl -L -o step_lightning_12500.pt https://huggingface.co/dilip025/dummy-model/resolve/d8269403a2597ca8b68cc65bafd2aabc3cce4fb8/ir_checkpoints/step_lightning_12500.pt
3.39 GB
- Xet hash:
- aeed14896b497b4e65bafeb90943912d2c62fe966b05c5da6332f09fe1faa4cc
- Size of remote file:
- 3.39 GB
- SHA256:
- 63cbdcd40a2c355671a2f0c80b1dfe3aaf89d3e24db945ab64b6f1d32790f114
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