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_tiny_checkpoints/step_lightning_900.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.64 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bed7868b5b597346403832fe9f033b6e4f506afb/satori_tiny_checkpoints/step_lightning_900.pt
- Command line
-
hf download hf://dilip025/dummy-model@bed7868b5b597346403832fe9f033b6e4f506afb/satori_tiny_checkpoints/step_lightning_900.pt
-
curl -L -o step_lightning_900.pt https://huggingface.co/dilip025/dummy-model/resolve/bed7868b5b597346403832fe9f033b6e4f506afb/satori_tiny_checkpoints/step_lightning_900.pt
1.64 GB
- Xet hash:
- 0b78805f44fb3a8d3a72bc2029b19fae8a867fadba7a8cac1d4a48efb04d2caa
- Size of remote file:
- 1.64 GB
- SHA256:
- 0ce275796216f9e2f56740bc46ea0d75b42ae112b52f2e6ec09cc41a64588093
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