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_2600.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/71921c912c083fe7020b435dfcf99d3ba952f7c4/satori_small_checkpoints/step_lightning_2600.pt
- Command line
-
hf download hf://dilip025/dummy-model@71921c912c083fe7020b435dfcf99d3ba952f7c4/satori_small_checkpoints/step_lightning_2600.pt
-
curl -L -o step_lightning_2600.pt https://huggingface.co/dilip025/dummy-model/resolve/71921c912c083fe7020b435dfcf99d3ba952f7c4/satori_small_checkpoints/step_lightning_2600.pt
6.29 GB
- Xet hash:
- ca15c24014e369515d0e5be2544524f164b29dc6f5044408b60a78c30bb35296
- Size of remote file:
- 6.29 GB
- SHA256:
- 8ab8ba0ddbfa77a7a412f0784060fa830647030a429c3f519453a07b18e30c07
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