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_prajna_checkpoints/step_lightning_8000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/48eda8ed471559e0d4b0f6da4c7ecdc11dbdc80a/satori_prajna_checkpoints/step_lightning_8000.pt
- Command line
-
hf download hf://dilip025/dummy-model@48eda8ed471559e0d4b0f6da4c7ecdc11dbdc80a/satori_prajna_checkpoints/step_lightning_8000.pt
-
curl -L -o step_lightning_8000.pt https://huggingface.co/dilip025/dummy-model/resolve/48eda8ed471559e0d4b0f6da4c7ecdc11dbdc80a/satori_prajna_checkpoints/step_lightning_8000.pt
2.81 GB
- Xet hash:
- 16a29601899f5b269d1858d4b399e351099fbdc96a3e8bd880bc40820ed982ca
- Size of remote file:
- 2.81 GB
- SHA256:
- 87b8970e677fe3fe7a275a6ff5cf661d4285077b0389a1ce7e685d52fd3ab435
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