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 satori_tiny_checkpoints/step_lightning_11000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_lightning_11000.pt
- Command line
-
hf download hf://dilip025/dummy-model@24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_lightning_11000.pt
-
curl -L -o step_lightning_11000.pt https://huggingface.co/dilip025/dummy-model/resolve/24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_lightning_11000.pt
3.99 GB
- Xet hash:
- b595edfa102f521b14cd77bdee951b92f431fcc511bbd29b471c35b8a1f8e08c
- Size of remote file:
- 3.99 GB
- SHA256:
- 0847c3e027daf759006216ef1822d12b1225324e2d81f2e992f0651b8bcec5b7
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