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_tienie_checkpoints/step_lightning_3000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.26 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/111f6847a46f2caeac8a920f43da08caf2ecbfef/satori_tienie_checkpoints/step_lightning_3000.pt
- Command line
-
hf download hf://dilip025/dummy-model@111f6847a46f2caeac8a920f43da08caf2ecbfef/satori_tienie_checkpoints/step_lightning_3000.pt
-
curl -L -o step_lightning_3000.pt https://huggingface.co/dilip025/dummy-model/resolve/111f6847a46f2caeac8a920f43da08caf2ecbfef/satori_tienie_checkpoints/step_lightning_3000.pt
2.26 GB
- Xet hash:
- c5987926a2506784e2cc6a6772e140fce0257738ecffaedd27729b4614182bf7
- Size of remote file:
- 2.26 GB
- SHA256:
- 6db07c9dad3a658b1daa0f06070670c8e8d1432ca75e39f03504350983ef8364
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