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_400.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_400.pt
- Command line
-
hf download hf://dilip025/dummy-model@bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_400.pt
-
curl -L -o step_lightning_400.pt https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_400.pt
4.49 GB
- Xet hash:
- aa80a0084c15e518784bf6f79858f07bfa26924dfabb91580ec0b226a0957036
- Size of remote file:
- 4.49 GB
- SHA256:
- ebda81a21c1fcf3602707ba6ff3c62ece9708731390aecec79beb25f5e698f64
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