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_v2/step_lightning_3500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_v2/step_lightning_3500.pt
- Command line
-
hf download hf://dilip025/dummy-model@7837c07ea958e6aabde620fbf35619d8fe048737/satori_v2/step_lightning_3500.pt
-
curl -L -o step_lightning_3500.pt https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_v2/step_lightning_3500.pt
4.35 GB
- Xet hash:
- dc2e95f5d0ac510cfea146b06eb5e3ab3994ed6c3340a8720514e9b432ddd624
- Size of remote file:
- 4.35 GB
- SHA256:
- 3e26d26d4e6794e2bedaf13a96a7d11b75d18835d6e9fdf9df56edfd92ca7fd5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.