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_7800.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/63b952ddf1cfc8cc42f9de30ee045b0acc518b57/satori_small_checkpoints/step_lightning_7800.pt
- Command line
-
hf download hf://dilip025/dummy-model@63b952ddf1cfc8cc42f9de30ee045b0acc518b57/satori_small_checkpoints/step_lightning_7800.pt
-
curl -L -o step_lightning_7800.pt https://huggingface.co/dilip025/dummy-model/resolve/63b952ddf1cfc8cc42f9de30ee045b0acc518b57/satori_small_checkpoints/step_lightning_7800.pt
6.29 GB
- Xet hash:
- 171f83652b0cf2530a86958bd5bd114f441212f7c73348b99eb6de2a18e5545f
- Size of remote file:
- 6.29 GB
- SHA256:
- 75dbb30a3ca3f2934ddfc9d4ff5a82676f99214df3e0bb03a0e2c1640292e907
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