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 satori_v2/step_lightning_7500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_v2/step_lightning_7500.pt
- Command line
-
hf download hf://dilip025/dummy-model@7837c07ea958e6aabde620fbf35619d8fe048737/satori_v2/step_lightning_7500.pt
-
curl -L -o step_lightning_7500.pt https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_v2/step_lightning_7500.pt
4.35 GB
- Xet hash:
- ee9a5fc6d9269c41224c1fb9711e6fb015a16185feec853794de2a4c87d35292
- Size of remote file:
- 4.35 GB
- SHA256:
- 665b81f12c8d11782e2f9abb206f5e59b31d8275ac7af939ce0d106c09546c54
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