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_v2/step_lightning_2500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_v2/step_lightning_2500.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_v2/step_lightning_2500.pt
-
curl -L -o step_lightning_2500.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_v2/step_lightning_2500.pt
4.35 GB
- Xet hash:
- 2473a2adebc287a601e1e66b8d0a62e704d2e8d08cbd87b4167bd5b7c450794f
- Size of remote file:
- 4.35 GB
- SHA256:
- bca61d8f60d10d60cd18fe7707b510a4700849e829e29f34641d31aaeccd1844
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