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_mid_checkpoints/step_lightning_200.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_mid_checkpoints/step_lightning_200.pt
- Command line
-
hf download hf://dilip025/dummy-model@7837c07ea958e6aabde620fbf35619d8fe048737/satori_mid_checkpoints/step_lightning_200.pt
-
curl -L -o step_lightning_200.pt https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_mid_checkpoints/step_lightning_200.pt
4.49 GB
- Xet hash:
- fd3de456cf4e38d3a57ed47b28f69c000c24072b996c3c1b0395550bbbd702bc
- Size of remote file:
- 4.49 GB
- SHA256:
- 6d94868fb25d2b2234f3e8fe61ce2580b8c670aa07f1079c16971d4905c90da8
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