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_small_checkpoints/step_lightning_6400.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/365c8d061d0d476e7077fa90c5aba3e51dda6c58/satori_small_checkpoints/step_lightning_6400.pt
- Command line
-
hf download hf://dilip025/dummy-model@365c8d061d0d476e7077fa90c5aba3e51dda6c58/satori_small_checkpoints/step_lightning_6400.pt
-
curl -L -o step_lightning_6400.pt https://huggingface.co/dilip025/dummy-model/resolve/365c8d061d0d476e7077fa90c5aba3e51dda6c58/satori_small_checkpoints/step_lightning_6400.pt
6.29 GB
- Xet hash:
- f49aac9aebee1ecd4d03152f94f04fd0268f626cf66742007d2924fb26221b31
- Size of remote file:
- 6.29 GB
- SHA256:
- c1798012605fed8340d34137d2cf9cf502ac2c633defc1bf71fd49173d099260
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