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_prajna_checkpoints/step_lightning_11000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/satori_prajna_checkpoints/step_lightning_11000.pt
- Command line
-
hf download hf://dilip025/dummy-model@b0cabf8afeef7eb6b72f215e699e99e655515084/satori_prajna_checkpoints/step_lightning_11000.pt
-
curl -L -o step_lightning_11000.pt https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/satori_prajna_checkpoints/step_lightning_11000.pt
2.81 GB
- Xet hash:
- 12f3ed1042ca502616be48807e04c3c63da3fcd81d486266db8ccaf49d12ed8d
- Size of remote file:
- 2.81 GB
- SHA256:
- f7ba0261738841b92ad82fa97f3ee9dd40ae62a0b163660f9e5409715f0b9d05
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