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_small_checkpoints/step_lightning_800.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/a7a00de86dc371e26e7bf7e16a2b76b6a91c6337/satori_small_checkpoints/step_lightning_800.pt
- Command line
-
hf download hf://dilip025/dummy-model@a7a00de86dc371e26e7bf7e16a2b76b6a91c6337/satori_small_checkpoints/step_lightning_800.pt
-
curl -L -o step_lightning_800.pt https://huggingface.co/dilip025/dummy-model/resolve/a7a00de86dc371e26e7bf7e16a2b76b6a91c6337/satori_small_checkpoints/step_lightning_800.pt
6.29 GB
- Xet hash:
- 858c0d795b995fd7b1ae84d42cde6a2c4df6a7a4ab251ec747a7975a53468fc7
- Size of remote file:
- 6.29 GB
- SHA256:
- a50661eaf96b966034a69abb1116a71cc2aa658f16149ab32ae23abc2a5ca00a
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