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_17500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_v2/step_lightning_17500.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_v2/step_lightning_17500.pt
-
curl -L -o step_lightning_17500.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_v2/step_lightning_17500.pt
4.35 GB
- Xet hash:
- 1a599455e4be38552bd68ed21f83ff9de627fb7ba4f61d9463b50a30848363f6
- Size of remote file:
- 4.35 GB
- SHA256:
- 190d7e009e261ea1aae483ffccf0d00318c9ff575741e34a728e4a1ecc7b146d
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